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Shete, Dipanshu. "Diet Recommendation System Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41831.
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In today’s modern world people all around the globe are becoming more interested in their health and lifestyle. But just avoiding junk food and doing an exercise is not enough, we require a balanced diet. A balanced diet based on our height, weight and age can lead a healthy life. Combined with physical activity, your diet can help you to reach and maintain a healthy weight, reduce your risk of chronic diseases (like heart disease and cancer), and promote your overall health. A balanced diet is one that gives your body the nutrients it needs to function correctly. Calories in the food is the measure of amount of energy store in that food. Our body use calories for basically everything like breathing, walking, running etc. On average a person needs 2000 calories per day but specifically intake of calories depends upon persons physical aspects like weight, height, age and gender. The fast-food consumption rate is alarmingly high and this consequently has led to the intake of unhealthy food. This leads to various health issues such as obesity, diabetes, an increase in blood pressure etc. Hence it has become very essential for people to have a good balanced nutritional healthy diet. Keywords — Machine Learning, Diet Recommendation, Personalized Nutrition, Health, BMI Calculation, Calorie Calculation, Nutritional Content Analysis.
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Kansagara, Harsh. "Diet and Workout Recommendation System Using KNN." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45702.
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Abstract— The “Diet and Workout Recommendation System” is a personalized platform that provides real-time dietary and fitness guidance based on individual user profiles. Through an intuitive web interface, the system collects data, including age, weight, height, fitness goals, and dietary preferences. The backend processes this data with API integrations and machine learning techniques, including a K-Nearest Neighbors (KNN) model, to generate dynamic meal and workout plans that adapt to user feedback and progress. Using OpenCV for exercise tracking and Flask for efficient data handling, the system delivers instant workout feedback and personalized dietary suggestions. Additionally, location-based APIs recommend local dining options, connecting digital health guidance with real-world choices. Unlike one-size-fits-all platforms, this system emphasizes personalization and adaptability to address the limitations of traditional health tools, offering scalable, user-centered recommendations. The architecture supports future integrations with wearable devices and social features, promoting sustainable lifestyle changes and long-term engagement. This project demonstrates a comprehensive approach to personalized health management, focusing on usability, real-world application, and an evolving user experience. Keywords— Health Recommendation System, Diet Planning, Workout Recommendation, Personalization, Nutritional Analysis, Strength Assessment, User Profiling, Wellness and Fitness.
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Chen, Rung-Ching, Chung-Yi Huang, and Yu-Hsien Ting. "A Chronic Disease Diet Recommendation System Based on Domain Ontology and Decision Tree." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 3 (2017): 474–82. http://dx.doi.org/10.20965/jaciii.2017.p0474.
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As society develops and science and technology improve, people have come to care more about a healthy diet. Diet types have gradually changed and focused more on health management. Taiwan is becoming an aging society in which individuals have irregular lifestyles, long-term unhealthy diets, stressful work, and chronic diseases such as diabetes, hypertension, and high cholesterol. However, most dietary recommendation systems cannot give dietary recommendations for patients with chronic diseases. Though healthy foods are recommended, the systems contain little information on whether nutrients are in balance. Therefore, this study constructed a diet recommendation system for chronic diseases using expert knowledge, which enables more convenient and precise dietary recommendations for chronic diseases. In this study, we use an ontology, decision trees, and Jena to construct the recommendation system. The dietary recommendations results are evaluated by dietitians, and the verification accuracy is 100%. Therefore, this system of dietary recommendations can provide convenient, healthy, dietary recommendations for nutrients for patients with chronic diseases.
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Rakhman, Raihan Romzi, and Dana Sulistyo Kusumo. "User-Centric Diet Recommender Systems with Human-Recommender System Interaction (HRI) based Serendipity Aspect." Building of Informatics, Technology and Science (BITS) 6, no. 2 (2024): 1020–33. https://doi.org/10.47065/bits.v6i2.5754.
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Currently, obesity is on the rise globally with predictions to continue rising until 2030. Adopting a healthy diet and increasing physical activity are key strategies to reduce the risk of obesity. However, there are significant challenges in adhering to a diet, including the monotony of food choices and difficulty in maintaining motivation. This research aims to develop a user-centered dietary recommendation system that addresses these challenges by introducing serendipity into the diet planning process. Serendipity in this context refers to generating unexpected yet relevant food recommendations, thereby enhancing user engagement and satisfaction. The system uses content-based recommendation techniques, including TF-IDF, Cosine Similarity, and K-Means clustering, to provide personalized dietary suggestions based on individual health profiles, calorie needs, and food preferences. The evaluation of the system demonstrated that incorporating serendipity into recommendations significantly improves user experience and adherence to dietary plans. The findings highlight the potential of serendipity to transform dietary adherence, making the dieting process more enjoyable and sustainable.
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Reddy, K. Sneha. "Diet Recommendation System Based On Vitamin Intake." Advances in Computational Sciences and Technology 16, no. 1 (2023): 35–43. http://dx.doi.org/10.37622/acst/16.1.2023.35-43.
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Uma, Pyla. "DIET PLANNING AND RECOMMENDATION SYSTEM USING ML AND MERN STACK." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32364.
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The "Diet Planning and Recommendation System Using ML and MERN Stack" project aims to develop an innovative solution to address the challenge of personalized diet planning and recommendation. With the rising awareness of the importance of nutrition in maintaining overall health and wellness, there is a growing demand for tools that can offer tailored dietary guidance to individuals based on their unique needs and preferences. This project leverages the power of Machine Learning (ML) algorithms and the MERN (MongoDB, Express.js, React.js, Node.js) stack to create a comprehensive and user-friendly platform. The system collects user data encompassing demographic information, health metrics, dietary habits, and goals. Using ML techniques such as regression, classification, and clustering, the system analyzes this data to generate personalized diet plans and recommendations. The backend of the system, built on Node.js and Express.js, manages data storage and processing, while the frontend, developed with React.js, provides an intuitive interface for users to interact with the system. MongoDB serves as the database, ensuring scalability and flexibility in data management. The ML models continuously learn and adapt based on user feedback and outcomes, enhancing the accuracy and effectiveness of the recommendations over time. Reinforcement learning techniques are employed to optimize diet plans based on real-world outcomes and user satisfaction. By integrating ML with the MERN stack, this project offers a novel approach to diet planning and recommendation, empowering individuals to make informed dietary choices and improve their overall health and well-being. KeyWords:Diet Planning ,Recommendation System,Machine Learning,(ML),MERNStack,Personalized,Nutrition,Health,,,Metrics,Dietary,Habits,Regression,Classification,Node.js, ,React.js,,MongoDB,User,Feedback,ReinforcementLearni,Real-world Outcomes,User Satisfaction,Wellness,Informed Dietary Choices
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Khopkar, Prof P. V. "Fit Fusion : Diet & Workout Recommendation." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47062.
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Abstract: In today’s fast-paced world, maintaining a healthy lifestyle through balanced diet and regular exercise is challenging for many individuals. Generic fitness plans often lack the personalization needed to support diverse body types and lifestyles. This paper presents FitFusion, a web-based system designed to offer personalized diet and workout recommendations based on individual characteristics such as age, weight, height, and dietary preferences. The system integrates machine learning, specifically the Random Forest algorithm, to predict and recommend diet plans based on user data and nutritional requirements. Developed using ReactJS, Express.js, and MongoDB, the platform includes a BMI calculator, intelligent recommendation engines, and a progress tracker. FitFusion aims to enhance user health outcomes and long-term adherence to healthy habits through real-time, personalized guidance.
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Archana, S., Kumar N. Harish, M. K. KavinNandha, K. KeerthiRaghavan, R. Keren, and Rishawanth L. Liyander. "Smart Health Monitoring and Recommendation System." Recent Trends in Androids and IOS Applications 7 (May 30, 2025): 38–51. https://doi.org/10.5281/zenodo.15550182.
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<em>The Smart Health Monitoring and Recommendation System is an intuitive and comprehensive mobile application designed to enhance healthcare through continuous monitoring, personalized recommendations, and doctor-patient interaction. Tailored for both patients and medical professionals, the app leverages user input and sensor data to monitor health metrics like glucose levels, medication intake, physical activity, diet, and more. With specialized dashboards for patients and doctors, it fosters timely communication, improved decision-making, and better overall health outcomes..</em>
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Lakshmi, N. Naga, M. Jagadeesh Reddy, K. Hari Krishna, and S. Sindhuja Reddy. "Vitamin Deficiency and Food Recommendation System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3823–30. http://dx.doi.org/10.22214/ijraset.2022.43236.
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Abstract: People are not paying attention to the quality of food they eat in our fast-paced and hectic world. They frequently ignore their eating routines and behaviours. Fast-food consumption is frighteningly increasing, which has resulted in the consumption of harmful foods. This causes a variety of health problems, including obesity, diabetes, and an increase in blood pressure, and so forth. As a result, it has become critical for people to have a well-balanced nutritionally sound diet. There are several applications that are thriving to assist folks in gaining control of their food and therefore can help individuals lose weight or maintain their fitness and health. The study article proposes healthy eating habits and patterns so that anybody may know the number of calories expended, macronutrient intake, and so on using data mining technologies. This technology is designed to uncover hidden patterns and client eating habits from various data sources. This approach will aid in tracking and improving an individual's health as well as the types of food that they should avoid in order to reduce their chance of disease. A balanced diet is one in which the intake of each basic nutrient meets its sufficient demand and real caloric intake equals calories burnt. Additionally, making a variety of dietary choices is vital for lowering the chance of acquiring chronic illnesses. This diet recommendation system tailors its recommendations to each individual depending on their eating patterns and body data. This study aids in the prediction of a healthy diet for any individual, as well as the construction of a diet plan based on the needs of the patient. Keywords : BMR, Healthy Diet, Recommender System, Harris Benedict equation, Nutrition, Calories, Data mining
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Thakar, Ajay. "Virtual Dietician for Diet Plan Recommendation." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 111–13. http://dx.doi.org/10.22214/ijraset.2021.34864.
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In this fast and busy schedule life, people are not giving importance to the quality of food they are eating. They tend to neglect their eating patterns and habits. The fast-food consumption rate is alarmingly high and this consequently has led to the intake of unhealthy food. This leads to various health issues such as obesity, diabetes, an increase in blood pressure etc. Hence it has become very essential for people to have a good balanced nutritional healthy diet. There are many applications which are booming to help people so that they can have control over their diet and hence can reduce weight or they can help them to keep them fit and healthy. The project is proposing healthy food habits and dieting patterns so that anyone can know the number of calories burned, the intake of macro nutrients and so on using on data mining tools. This tool is used for discovering hidden patterns and customer eating habits from different types of data sources. This system will help in tracking and improving the individual’s health and the type of food which they can avoid leading towards the risk of illness. A balanced diet means that the intake of each necessary nutrient meets its adequate demand and actual caloric intake balances with calories burned. Additionally, making a diversity of choices from various types of food is also essential to reduce the risk of developing chronic diseases. This diet recommended system focuses on every individual based on their eating habits and body statistics. This research helps in the prediction of a healthy diet for any individual and nutrition is to doctor to design a diet plan as per patient’s need.
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Zingade, D. S., Umar Shaikh, Shreyas Saisekhar, Umang Koul, and Keshav Vaswani. "An Online Diet Recommendation System Based On Artificial Intelligence." International Journal of Computer Sciences and Engineering 7, no. 4 (2019): 1126–30. http://dx.doi.org/10.26438/ijcse/v7i4.11261130.
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Nagur Vali Shaik, Lokesh Karthik Varma Penmetsa, Spandana Salveru, Praisey Bathula, and Sahas Manikanta Madishetty. "Medicine recommendation system (Health Harbour)." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 195–203. https://doi.org/10.30574/wjarr.2025.25.2.0380.
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Health Harbour is a machine-learning-based system designed to assist users in identifying possible health conditions and finding suitable medications. By analyzing symptoms entered by the user, it simplifies the process of symptom-based diagnosis and provides helpful health insights. Built using Python and Scikit-Learn, the model is trained on a dataset of 187 symptoms and achieves an impressive accuracy of 99.6%. Users can input four key symptoms, and the system will predict potential illnesses while suggesting appropriate medications. Additionally, it offers diet recommendations, necessary precautions, and workout plans to promote overall well-being. With an easy-to-use interface powered by Stream-Lit, Health Harbour ensures a smooth and interactive experience. By making healthcare guidance more accessible, this system helps users take proactive steps toward better health and informed decision-making.
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Nagur Vali Shaik, Lokesh Karthik Varma Penmetsa, Spandana Salveru, Praisey Bathula, and Sahas Manikanta Madishetty. "Medicine recommendation system (Health Harbour)." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 195–203. https://doi.org/10.30574/wjarr.2025.25.2.0382.
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Health Harbour is a machine-learning-based system designed to assist users in identifying possible health conditions and finding suitable medications. By analyzing symptoms entered by the user, it simplifies the process of symptom-based diagnosis and provides helpful health insights. Built using Python and Scikit-Learn, the model is trained on a dataset of 187 symptoms and achieves an impressive accuracy of 99.6%. Users can input four key symptoms, and the system will predict potential illnesses while suggesting appropriate medications. Additionally, it offers diet recommendations, necessary precautions, and workout plans to promote overall well-being. With an easy-to-use interface powered by Stream-Lit, Health Harbour ensures a smooth and interactive experience. By making healthcare guidance more accessible, this system helps users take proactive steps toward better health and informed decision-making.
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Minal, Pardey Yogita Puttewar* Janhavi Keche Vaishnavi Paghrut Vaishnavi Jayale. "Research on Nutrition Deficiency Analysis and Diet Plan Recommendation System." International Journal of Scientific Research and Technology 2, no. 5 (2025): 237–48. https://doi.org/10.5281/zenodo.15385892.
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Nutrition is the source of energy that is required to carry out all the processes of the human body. “Nutritional deficiency” consists of severely reduced levels of one or more nutrients, making the body unable to normally perform its functions and thus leading to an increased risk of several diseases like cancer, diabetes, and heart disease. This paper presents a Nutrition Deficiency Analysis and Diet Plan Recommendation System developed using Python with a backend SQLite3 database and deployed through Flask. The system is designed to identify nutritional deficiencies and generate personalized diet plans based on user-provided data, including medical history, dietary habits, symptoms, genetic predispositions, and lifestyle factors. By leveraging machine learning techniques, the system analyzes this data to detect imbalances in essential nutrients and visualizes the results through a nutrient deficiency graph. It then recommends tailored meal plans using a rich knowledge base of nutritional information, ensuring science-backed dietary guidance. This paper aids in the construction of a diet plan based on the needs of the user.
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Nidhi Waghela, Jahanvi Mistry, Melony Bharucha, and Ms. Monali Parikh. "Diet Recommendation System Using K-Means Clustering Algorithm of Machine Learning." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 567–71. http://dx.doi.org/10.32628/cseit2410445.
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In today’s world, many people suffer from range of illnesses due to lack of nutrients in their daily diet. It’s not always simple to recommend diet right away. The majority of individuals in the today’s world are fanatically trying to reduce weight, gain weight, or keep their health in check. The study relies on a database that has various amount of nutrients. As a result of the circumstance, we set out to create a program that would help out individuals to become healthy. Only three orts of good are recommended weight loss, weight gain, and staying healthy. The diet recommendation system leverages the user input such as, name, age, height, weight which calculate BMI and provide necessary diet based on the option of vegetarian or non-vegetarian meals from three categories which are weight gain, weight loss, and staying healthy. We’ll discuss about the classification of food based on machine learning in this post. This research includes K-means Clustering algorithm for future diet plan prediction.
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Naik, Pratiksha Ashok. "Intelligent Food Recommendation System Using Machine Learning." Volume 5 - 2020, Issue 8 - August 5, no. 8 (2020): 616–19. http://dx.doi.org/10.38124/ijisrt20aug414.
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The buying behavior of the consumer is affected by the suggestions given to the items. Recommendations can be made in the form of a review or ranking given to a specific product. Calories consumed by people contains carbohydrates, fats, proteins, minerals and vitamins, and any malnutrition causes severe health problems. In this paper, we propose a recommendation system which is trained on the basis of the recommendations received by the customer who has already used the product. Software recommends the product to the customer on the basis of the experience of the consumer using the same product. Each person has his or her own eating patterns, based on the preferences and dislikes of the user, indicating that personalized diet is important to sustain the success and health of the user. The proposed recommendation method uses a deep learning algorithm and a genetic algorithm to provide the best possible advice.
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Kingsley Orevaoghene, Eniforo, Chika Yinka-Banjo, and Emmanuel John Anagu. "Fuzzy Logic Based Personalised Diet Recommendation engine for Dietary Prevention and Control of Diabetes." Journal of Engineering, Computational and Applied Sciences (JECAS) 8, no. 1 (2024): 1–13. https://doi.org/10.64290/jecas.v8i1.894.
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Dietary management is very important not only to prevent Type 2 diabetes mellitus but also to treat it. Despite the fact that there are great strides in knowledge dietary strategies, many patients continue to rely on generic advice instead of individualized plans. There is limited integration of technology-based tools into routine care to assist healthcare providers in delivering personalized dietary recommendations. This research developed a personalized system that aligns dietary recommendations with the health conditions and preferences of diabetes patients. The system used Fuzzy Logic interpretability capabilities for both diabetes prediction and dietary recommendation generation. The system derives its framework from the Nigerian food composition table thus providing culturally appropriate advice to its users. The FL system transforms prediction outputs to dietary recommendations according to rules defined by three predictive factors of HbA1c, BMI and Age thus enabling clinical translation from diagnosis to therapeutic actions. Through the Flask-based web interface patients can submit patient information to obtain performance scores together with personalized dietary recommendation in real time. The recommendation helps people better manage their blood sugar and overall health, which is essential for preventing serious long-term complications. It offers a more effective way to give dietary advice, improving patient outcomes and making healthcare more efficient.
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Sree, A. Varsha. "HEALTH DIET PLANNER USING PYTHON." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04548.
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Abstract— This project implements a machine learning-based diet recommendation system using a Decision Tree Classifier. The model is trained on a dataset containing various health parameters, including age, gender, BMI, disease type, severity, physical activity level, and dietary restrictions, to predict an appropriate dietary plan. The system encodes categorical variables, splits the dataset for training and testing, and achieves predictions with high accuracy. The trained model is saved for future use, enabling real-time diet recommendations based on user input. The model suggests personalized meal plans for categories like Low-Carb, Low-Sodium, and Balanced diets, assisting individuals in maintaining optimal health through tailored nutritional guidance. Keywords: Age, BMI, Decision Tree, Health, Support Vector Machines (SVM),
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Chhipa, Shubham, Vishal Berwal, Tushar Hirapure, and Soumi Banerjee. "Recipe Recommendation System Using TF-IDF." ITM Web of Conferences 44 (2022): 02006. http://dx.doi.org/10.1051/itmconf/20224402006.
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A Recipe Recommendation System is being proposed in this following paper. Food recommendation is a new area, with few systems that are focus on analysing and user preferences and constraints such as ingredients available at their side being deployed in real settings in the form of web application or mobile application [4]. The proposed model is a mobile application which allows users to search recipes using ingredients available at them including vegetables. For this work we have find a dataset which is a collection of Indian cuisines recipes and apply the content-based recommendation using Term Frequency – Inverse Document Frequency (TF-IDF) and Cosine Similarity [1]. This application gives the recommendation of Indian recipes based on ingredients available at them and allows users to filter out the recipes on course type, diet type, etc.
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Akinbohun, Folake. "Development of Models by Energy Expended and Age Classifications for Diet Recommendation System." European Journal of Computer Science and Information Technology 12, no. 6 (2024): 24–34. http://dx.doi.org/10.37745/ejcsit.2013/vol12n62434.
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People today are more conscious of their health and constantly looking for methods to improve their health status. Due to the unavailability and inaccessibility of dieticians to recommend diet, people do not know how to plan their diet well, thereby people’s health are compromised because of unbalanced diet and misappropriation of diet. There is need to develop models that recommend diet on various classifications on the basis of energy expended daily and age. The models are mathematically represented using arithmetic expressions on different classifications: Energy expended or job/activities, health status, family size and age. The models use the rule-based statements to proffer solution to diet problems. In employing the technology, the models were implemented using a programming tool like PhP etc The DRS can be deployed on the World Wide Web and be allowed to be used by the general public for a healthy diet. The Diet Recommendation System (DRS) creates and increases people's awareness and assist them in receiving appropriate counsel as to the quantity of food, food types, time to take food, the appropriate food for certain age group, the balanced diet for a family size, the kind of food type according to the activities a person engages in.
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Wadhwan, Ankita, Priyanka Chawla, Sandeep Kaur, and Usha Mittal. "IntelliHealth: A Machine Learning Driven Disease Detectionand Diet recommendation System." Journal of Computer Science 21, no. 6 (2025): 1251–65. https://doi.org/10.3844/jcssp.2025.1251.1265.
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Samita Bhandari, Simon Guljarilal Bansal, Sushmitha Santhosh, and Isha Pramod Lakhekar. "AI-Powered Fitness and Diet Recommendation System: A Personalized Approach to Health and Wellness." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 03 (2025): 534–39. https://doi.org/10.47392/irjaem.2025.0085.
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With the rise of technology in healthcare, personalized fitness and diet recommendations have gained significant attention. This paper presents an AI-powered fitness and diet recommendation system that leverages machine learning (ML) and generative AI to provide tailored workout plans, meal suggestions, and health-tracking features[1]. The system analyzes user-specific parameters such as age, weight, height, fitness goals, and dietary preferences to generate customized recommendations. Implemented using React, Redux, Node.js, Flask, and MongoDB, the platform integrates AI-driven insights to enhance user engagement through gamification, social sharing, and progress tracking. Initial evaluations demonstrate the system's effectiveness in offering personalized and adaptive health recommendations. This study highlights the potential of AI in promoting healthier lifestyles and outlines future improvements to enhance accuracy and user experience[2]
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Manoharan, Dr Samuel, and Prof. Sathish. "Patient Diet Recommendation System Using K Clique and Deep learning Classifiers." June 2020 2, no. 2 (2020): 121–30. http://dx.doi.org/10.36548/jaicn.2020.2.005.
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There are several systems designed for the purpose of recommending. The recommending system has gained its prominence even in the medical industry for suggesting the diets for the patient’s, medicines to be taken, treatments to be taken etc. The recommendation system mainly enhances the robustness, extends protection against the many disease and improves the quality of living of an individual. So to automatically suggest the foods based on their health conditions and the level of sugar, blood pressure, protein, fat, cholesterol, age etc. the paper puts forth k-clique embedded deep learning classifier recommendation system for suggesting the diets for the patients. The K-clique incorporated in the recommendation system in an effort of getting an improved preciseness and increasing the accuracy of the deep learning classifier (gated recurrent units). The dataset for the empirical analysis of the developed system was performed with the data set of the patients collected over the internet as well as hospitals, information’s of about 50 patients were collected with thirteen features of various disease and thousand products with eight feature set. All these features were encoded and grouped into several clusters before applying into the deep learning classifiers. The better preciseness and the accuracy observed for the developed system experimentally is compared with the machine learning techniques such as logistic regression and Naïve Bayes and other deep learning classifiers such as the MLP and RNN to demonstrate the proficiency of the K-clique deep learning classifier based recommendation system (K-DLRS)
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Khan, Abdus Salam, and Achim Hoffmann. "Building a case-based diet recommendation system without a knowledge engineer." Artificial Intelligence in Medicine 27, no. 2 (2003): 155–79. http://dx.doi.org/10.1016/s0933-3657(02)00113-6.
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J. Watane, Ms Rushali, and Prof Nitin R.Chopde. "A Survey on - Healthy Diet Recommendation System using Web Data Mining." International Journal of Engineering Trends and Technology 10, no. 2 (2014): 105–7. http://dx.doi.org/10.14445/22315381/ijett-v10p220.
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V., Shital, and S. S. Sambare. "Study of Diet Recommendation System based on Fuzzy Logic and Ontology." International Journal of Computer Applications 132, no. 12 (2015): 20–24. http://dx.doi.org/10.5120/ijca2015907625.
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Orue-Saiz, Iñigo, Miguel Kazarez, and Amaia Mendez-Zorrilla. "Systematic Review of Nutritional Recommendation Systems." Applied Sciences 11, no. 24 (2021): 12069. http://dx.doi.org/10.3390/app112412069.
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In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society. There are many apps created to count calories based on what we eat, or to estimate calorie consumption according to the sport we do, or to recommend recipes, but very few are capable of giving personalized recommendations. This review tries to see what studies exist and what recommendation systems are used for this purpose, over the last 5 years in the main databases. Among the results obtained, it is observed that the existing works focus on the recommendation system (usually collaborative filtering), and not so much on the description of the data or the sample analyzed; the indices used for the calculation of calories or nutrients are not specified. Therefore, it is necessary to work with open data, or well-described data, which allows the experience to be reproduced by third parties, or at least to be comparable. In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society.
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Moz, Shahadat Hoshen, Md Apu Hosen, Md Noornobi Sohag Santo, Sk Shalauddin Kabir, Md Nasim Adnan, and Syed Md. Galib. "Precision cardiodiet: transforming cardiac care with artificial intelligence-driven dietary recommendations." Radioelectronic and Computer Systems, no. 4 (December 6, 2023): 20–31. http://dx.doi.org/10.32620/reks.2023.4.02.
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The subject matter of this research revolves around addressing the escalating global health threat posed by cardiovascular diseases, which have become a leading cause of mortality in recent times. The goal of this study was to develop a comprehensive diet recommendation system tailored explicitly for cardiac patients. The primary task of this study is to assist both medical practitioners and patients in developing effective dietary strategies to counter heart-related ailments. To achieve this goal, this study leverages the capabilities of machine learning (ML) to extract valuable insights from extensive datasets. This approach involves creating a sophisticated diet recommendation framework using diverse ML techniques. These techniques are meticulously applied to analyze data and identify optimal dietary choices for individuals with cardiac concerns. In pursuit of actionable dietary recommendations, classification algorithms are employed instead of clustering. These algorithms categorize foods as "heart-healthy" or "not heart-healthy," aligned with cardiac patients’ specific needs. In addition, this study delves into the intricate dynamics between different food items, exploring interactions such as the effects of combining protein- and carbohydrate-rich diets. This exploration serves as a focal point for in-depth data mining, offering nuanced perspectives on dietary patterns and their impact on heart health. The method used central to the diet recommendation system is the implementation of the Neural Random Forest algorithm, which serves as the cornerstone for generating tailored dietary suggestions. To ensure the system’s robustness and accuracy, a comparative assessment involving other prominent ML algorithms—namely Random Forest, Naïve Bayes, Support Vector Machine, and Decision Tree, was conducted. The results of this analysis underscore the superiority of the proposed -based system, demonstrating higher overall accuracy in delivering precise dietary recommendations compared with its counterparts. In conclusion, this study introduces an advanced diet recommendation system using ML, with the potential to notably reduce cardiac disease risk. By providing evidence-based dietary guidance, the system benefits both healthcare professionals and patients, showcasing the transformative capacity of ML in healthcare. This study underscores the significance of meticulous data analysis in refining dietary decisions for individuals with cardiac conditions.
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Gallo, Ignazio, Nicola Landro, Riccardo La Grassa, and Andrea Turconi. "Food Recommendations for Reducing Water Footprint." Sustainability 14, no. 7 (2022): 3833. http://dx.doi.org/10.3390/su14073833.
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Most existing food-related research efforts focus on recipe retrieval, user preference-based food recommendation, kitchen assistance, or nutritional and caloric estimation of dishes, ignoring personalized and conscious food recommendations resources of the planet. Therefore, in this work, we present a personalized food recommendation scheme, mapping the ingredients to the most resource-friendly dishes on the planet and in particular, selecting recipes that contain ingredients that consume as little water as possible for their production. The system proposed here is able to understand the user’s behavior and to suggest tailor-made recipes with lower water quantity used in production. By continuously using the system, the user can gradually reduce their water footprint and benefit from a healthier diet. The proposed recommendation system was compared with the results of two papers available in the literature that represent the state of the art, obtaining similar results. Therefore, the results of the presented recommendation system can be considered reliable.
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Virgiani, Igga Febrian, Z. K. A. Baizal, and Ramanti Dharayani. "Healthy Menu Recommendation for Malnutrition Patients Based on Ontology." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 1 (2023): 392. http://dx.doi.org/10.30865/mib.v7i1.5543.
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A healthy diet is one of the keys to creating a healthy lifestyle, but at this time the selection of a healthy and nutritious meal menu in the society is difficult to do because of the limited nutritional information contained in a food. A healthy diet can help a person to get balanced nutrition, good nutritional intake can increase the body's immunity, and make a normal or healthy body weight so that it can increase work productivity and prevention of chronic diseases. To overcome this problem, we propose the use of ontology and Semantic Web Rule Language (SWRL) to build a healthy menu recommendation system in the form of a chatbot to make it easier for users to determine the daily meal menu. These recommendations are personalized by considering the user's needs. Ontology is used to represent the required knowledge and the reasoning process uses SWRL. From the results of system testing, the recommendations get the accuracy of the F-Score value of 0.951
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Roy, Febin, Ashish Shaji, Vinu Sherimon, and Malak Majid Salim Al Amri. "STAY-HEALTHY: AN EXPERT SYSTEM TO SUGGEST A HEALTHY DIET." International Journal of Engineering Science Technologies 6, no. 1 (2022): 11–17. http://dx.doi.org/10.29121/ijoest.v6.i1.2022.262.
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In this time of sudden outbreaks of illnesses and new viruses, people try to seek out more healthy and better lives to protect their fitness in all viable ways. As ways as an amateur character care, he/she isn't aware of the shape of ingredients and therefore the big variety of energy to eat which could lead on him/her to a healthful life, especially people that are suffering from persistent non-Communicable illnesses (NCD) which include cardiovascular illnesses, hypertension, diabetes etc. This study proposes the development of a knowledgeable gadget that shows a customized everyday weight loss program plan, specifically for citizens suffering from NCD. As a part of this study, we've developed a recommendation system considering the above facts. Recommendation systems are considered an efficient technology that helps users to regulate their healthy diet and be free from the NCDs.
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Joshi, Saurav, Filip Ilievski, and Jay Pujara. "Knowledge-Powered Recommendation for an Improved Diet Water Footprint." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23805–7. http://dx.doi.org/10.1609/aaai.v38i21.30571.
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According to WWF, 1.1 billion people lack access to water, and 2.7 billion experience water scarcity at least one month a year. By 2025, two-thirds of the world's population may be facing water shortages. This highlights the urgency of managing water usage efficiently, especially in water-intensive sectors like food. This paper proposes a recommendation engine, powered by knowledge graphs, aiming to facilitate sustainable and healthy food consumption. The engine recommends ingredient substitutes in user recipes that improve nutritional value and reduce environmental impact, particularly water footprint. The system architecture includes source identification, information extraction, schema alignment, knowledge graph construction, and user interface development. The research offers a promising tool for promoting healthier eating habits and contributing to water conservation efforts.
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Chang, I.-Cheng, Nguyen Minh Trang, Ken Chang, and Kenrick Albert. "Diet advisor: an image-based food intake analysis and meal recommendation system." IET Conference Proceedings 2024, no. 28 (2025): 37–39. https://doi.org/10.1049/icp.2025.0183.
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Jadhav, Swati, Sandip Shinde, Vivek Ghuge, Divija Godse, Mitrajeet Golsangi, and Pravin Harne. "A Software Development Lifecycle Case Study on: Diet Recommendation System based on User Activities." ITM Web of Conferences 50 (2022): 01009. http://dx.doi.org/10.1051/itmconf/20225001009.
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This paper focuses on the methods of software engineering which can be helpful to various people working on huge projects with teams. This paper considers diet application as a case study. The main aim of the proposed system (diet application) is to give its users a healthy and balanced diet. The application is made in Flutter to ensure it reaches most of the audience and maximum people can receive its benefits. This paper is mostly based on the process used to create the application and all the views, processes, and architecture of the same. Some of the methods which are mentioned in the paper are the implementation of product backlog for the proposed system in association with a planning poker activity. The paper also has various Unified Modelling Language (UML) views in order to explain the design, implementation, use case and deployment of the proposed system.
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Tang, Jianchen, Bing Huang, and Mingshan Xie. "Anticancer Recipe Recommendation Based on Cancer Dietary Knowledge Graph." European Journal of Cancer Care 2023 (October 18, 2023): 1–13. http://dx.doi.org/10.1155/2023/8816960.
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Many recipes contain ingredients with various anticancer effects, which can help users to prevent cancer, as well as provide treatment for cancer patients, effectively slowing the disease. Existing recipe knowledge graph recommendation systems obtain entity feature representations by mining latent connections between recipes and between users and recipes to enhance the performance of the recommendation system. However, it ignores the influence of time on user taste preferences, fails to capture the dependency between them from the user’s dietary records, and is unable to more accurately predict the user’s future recipes. We use the KGAT to obtain the embedding representation of entities, considering the influence of time on users, and recipe recommendation can be viewed as a long-term sequence prediction, introducing LSTM networks to dynamically adjust users’ personal taste preferences. Based on the user’s dietary records, we infer the user’s preference for the future diet. Combined with the cancer knowledge graph, we provide the user with diet recommendations that are beneficial to disease prevention and rehabilitation. To verify the effectiveness and rationality of PPKG, we compared it with three other recommendation algorithms on the self-created datasets, and the extensive experimental results demonstrate that our algorithm performance performs other algorithms, which confirmed the effectiveness of PPKG in dealing with sequence recommendation.
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K, Renuka Devi, Bhavithra J, and Saradha A. "DIET RECOMMENDATION FOR GLYCEMIC PATIENTS USING IMPROVED KMEANS AND KRILL-HERD OPTIMIZATION." ICTACT Journal on Soft Computing 10, no. 3 (2020): 2096–101. https://doi.org/10.21917/ijsc.2020.0298.
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Maintaining nutrition for glycemic (diabetic) patients in order to retain the blood glucose level is one of the important activity to be followed. Stimulating the amount of carbohydrates, protein, vitamins, and minerals will result in a healthy diet. So, there is a necessity for recommendation of nutrition to those diabetic patients nowadays. Recommender Systems (RS) play a vital role in urging relevant suggestions to the users. To promote improvised and optimized results, Optimization technique plays a significant role in refining the parameters of chosen algorithm. To optimize and to upgrade the accuracy of recommendations, the system has been developed by implementing improved Krill-Herd algorithm. The system which clusters the profiles of diabetic patients using improved k-means clustering algorithm and results has been optimized using Improved Krill-Herd optimization algorithm. The performance will be analysed using different measures like Precision, Recall, F-measure, Accuracy, Matthews correlation, Fallout rate and Miss rate. The evaluation results show that the proposed system performs better and produces optimized results to the diabetic patients with minimum error rate.
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Iwendi, Celestine, Suleman Khan, Joseph Henry Anajemba, Ali Kashif Bashir, and Fazal Noor. "Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model." IEEE Access 8 (2020): 28462–74. http://dx.doi.org/10.1109/access.2020.2968537.
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BEDERIANA, Shyti, Stergu ARGESTA, Valera DHURATA, and Papajani BLERINA. "Food Recommendation System for a Healthy Liver Using Machine Learning." Eurasia Proceedings of Science Technology Engineering and Mathematics 28 (August 15, 2024): 438–47. http://dx.doi.org/10.55549/epstem.1523634.
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Nowadays, the work routine, the problems we face, make us not pay proper attention to healthy eating. As a result, people get sick. We have created through mathematical knowledge (statistics, probability, linear algebra, geometry), combining the concepts of Machine Learning (Content Based Filtering, TD IDF Vectorizer Algorithm, Conditional Independence, Count Vectorization) with Python language, a Recommendation System for all people suffering from liver. Liver is one of the most important organs of our body, because it makes 500 essential functions in the organism. But we have to be aware about the types of food that we use. Also, we must be very careful with our lifestyle and diet on foods. The aim of this article is to recommend the most appropriate foods for a healthy liver. This article will help the p
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Ladyzhets, Viktor, and Svitlana Terenchuk. "Models, Methods and Tools of Planning Human Diet." Management of Development of Complex Systems, no. 53 (March 17, 2023): 39–44. http://dx.doi.org/10.32347/2412-9933.2023.53.39-44.
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In this article was taken to consideration an issue of planning a person's diet and nutrition and were determined factors that significantly affect the choice of food. It was covered approaches studies on the basis of which modern systems of recommendations for a balanced diet work. A classification of decision support systems regarding the choice of diet and diet is provided based on the data on the basis of which they provide recommendations. The limitations of the linear combination of the user's choice factors and the elements characterizing his profile are shown when modeling a large number of hidden factors affecting the user's diet. The main focus of the research is directed to the analysis of models and methods used in the development of intelligent systems decision support systems about balanced nutrition. The analysis of the advantages and disadvantages of intelligent recommendation systems showed the relevance of developing a complete system that can provide recommendations taking into account a large number of explicit and implicit factors affecting the user's diet and nutrition on his health. Based on the analysis of the models and methods of artificial intelligence already used in such systems, the perspective of the development of models and methods of machine learning has been substantiated and the field of interest for further research has been formed. These studies are planned to be addressed to the construction of a multilayer system of deep machine learning, which will be able to take into account a large number of factors that depend on a balanced diet and healthy eating of each user.
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Misbahul Munir, Muhammad, Ade Pujianto, and Haechal Aulia Muhali Lamuru. "Optimisasi Algoritma Genetika dengan Particle Swarm Optimization (PSO) untuk Sistem Rekomendasi Diet Gizi bagi Penderita Diabetes." Jurnal Riset Sistem dan Teknologi Informasi (RESTIA) 1, no. 2 (2023): 38–48. http://dx.doi.org/10.30787/restia.v1i2.1289.
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Diabetes, especially diabetic nephropathy, is a global health problem that is increasing in prevalence. This disease can cause various serious complications and even death. Despite the high cure rate associated with diabetes, it is important to improve the human body's immune system to reduce the risk of developing diabetes or diabetic nephropathy. One approach that can help is maintaining a diet with good nutritional coverage. This research aims to develop an artificial intelligence (AI) system that can provide recommendations for a good nutritional diet menu for diabetes sufferers. We propose the use of well-known genetic algorithms in decision making. However, to improve the accuracy and efficiency of the genetic algorithm, we will optimize it using the Particle Swarm Optimization (PSO) algorithm. The research method used is an experimental method, where we will conduct experiments to test the performance of the optimized genetic algorithm. It is hoped that the results of this research can be used as a basis for making scientific publications in accredited national journals as well as product patents for food menu recommendation systems for diabetes sufferers. The main contribution of this research is improving the performance of the genetic algorithm through the use of the PSO algorithm, which will help increase the accuracy of the nutritional diet recommendation system. In this way, it is hoped that the results of this research can provide significant benefits in efforts to prevent and manage diabetes and improve the quality of life of diabetes sufferers.
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Ter, Zheng Bin, Naveen Palanichamy, and Jayapradha J. "Generative AI-based Meal Recommender System." Journal of Informatics and Web Engineering 4, no. 2 (2025): 315–38. https://doi.org/10.33093/jiwe.2025.4.2.20.
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Maintaining a balanced diet is essential for overall well-being, yet many individuals face challenges in meal planning due to time constraints, limited nutritional knowledge, and difficulty aligning meals with personal dietary needs. Traditional meal recommender systems often rely on predefined plans or collaborative filtering techniques, limiting their adaptability and personalization. This study presents a generative AI-based Meal Recommender System utilizing Variational Autoencoders (VAEs) to generate personalized and nutritionally balanced meal plans. The system processes user inputs, such as dietary preferences, nutritional goals, and ingredient availability, to provide tailored recommendations. VAEs effectively uncover hidden dietary patterns and nutritional relationships within complex data, facilitating relevant and personalized meal suggestions. The system is trained and evaluated using two integrated datasets: one containing detailed nutritional information for complete meal plans, including attributes such as calories, protein, fats, carbohydrates, and sodium, and another listing individual dishes along with their names and user ratings. The meal plan dataset connects multiple dishes into structured daily meal schedules, while the dish dataset provides popularity and quality insights through user feedback. Together, these datasets enable the generation of personalized and nutritionally optimized meal recommendations. Experimental evaluation indicates strong ranking performance with a Normalized Discounted Cumulative Gain (NDCG) score of 0.963. However, Root Mean Square Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) scores of 47.77, 2282.32, and 36.28, respectively, highlight potential areas for improving nutritional accuracy. A practical comparison with existing meal recommendation applications demonstrates the VAE model’s advantages in terms of personalization, nutritional fine-tuning, and recommendation diversity. The research contributes to AI-driven nutrition planning, healthcare, and fitness, offering a scalable and intelligent solution for personalized dietary recommendations.
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Vipparla, Aruna. "CaviScanNet: AI-Powered Cavity Detection, Segmentation, and Diagnosis with BERT Recommendations." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50143.
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Abstract.This paper introduces a deep learning-based system for dental X-ray analysis aimed at automating cavity detection, severity classification, and providing personalized recommendations. Using Mask R-CNN, the system detects cavities and segments their affected areas, while ResNet-50 classifies the severity of caries into superficial, medium, or deep categories. A fine-tuned BERT-based recommendation system then offers tailored advice based on severity and potential causes such as poor hygiene or diet. The solution reduces manual diagnostic effort, enhances accuracy, and provides actionable insights, which can be deployed via a web interface for remote accessibility and clinical integration, thus advancing dental care and early intervention.The results shows that the detection is highly accurate with 89.2% mAP (Mean Average Precision), the segmentation accuracy by DSC (Dice Similarity Coefficient) was 91.5%, the classification produced 92% validation accuracy among the superficial, medium, deep caries and recommendation had a 90% relevance score matched with dentists advice which is a one-of-a-kind feature. Keywords: Deep learning, Mask R-CNN, ResNet-50, Image segmentation, Feature Extraction, BERT-based Recommendation System, Mean Average Precision, Dice Similarity Coefficient
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Xie, Weiguang, and Hongliang Lou. "Implementation of Key Technologies for a Healthy Food Culture Recommendation System Using Internet of Things." Mobile Information Systems 2022 (August 31, 2022): 1–12. http://dx.doi.org/10.1155/2022/9675452.
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With the development of catering culture, the types of diets are changing with each passing day. The types of food are becoming more and more abundant, and the concept of healthy eating has become more and more prominent in people’s thinking. The development of Internet of Things technology enables people to live a variety of information in the network, and the amount of information increases sharply. With the development of the Internet of Things, the number of online recipes has increased significantly, and a number of choices are now available to people to find suitable recipes. It is still troublesome for people to select the menu of three meals on daily basis. In recent years, many people have long-term unhealthy eating habits, which has greatly increased the incidence of some diseases. Therefore, a balanced diet is very important for good health. This paper builds a fusion of healthy eating culture with the help of Internet of Things, deep learning, and other related technologies. A more practical healthy diet recommendation system is built, which aims to help people find diet recipes that conform to their personal eating preferences and habits and their health status. Experimental results show that the healthy food culture recommendation system constructed in this paper can more accurately recommend suitable healthy food recipes for individuals and promote people’s health. And the launched recipes can ensure nutrition and meet individual taste preferences, which has great practical significance.
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Namgung, Kwon, Tae-Hwan Kim, and Youn-Sik Hong. "Menu Recommendation System Using Smart Plates for Well-balanced Diet Habits of Young Children." Wireless Communications and Mobile Computing 2019 (November 14, 2019): 1–10. http://dx.doi.org/10.1155/2019/7971381.
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A well-balanced diet habit of a wide variety of foods and adequate nutrition can help to maintain proper growth and healthy life for young children. In Korea, young children aged 3 to 6 years use their own plates to eat lunch served in the kindergarten or childcare facilities. In this paper, we propose a smart plate that can easily measure how much food children have eaten. The smart plate has five load cell sensors to measure the weight of five places. Using them, the amount of food intake can be determined by measuring the weight of food before and after meals, respectively. This helps to know which foods young children prefer and which foods they do not prefer and identifies nutritional deficiencies in them. In addition, long-term accumulated data can be used to predict the growth index of young children. Finally, menus are recommended based on the nutrition facts consumed on a monthly basis by analyzing nutrition ingredients that were insufficient or excessive for children.
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Olutunde, Timothy, Chukwuemeka Lawrence Ani, and Godwin Aondofa Adesue. "Leveraging Machine Learning for Personalized Dietary Recommendations, Nutritional Patterns, and Health Outcome Predictions." Journal of Science Research and Reviews 1, no. 2 (2024): 43–56. https://doi.org/10.70882/josrar.2024.v1i2.40.
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Unhealthy dietary patterns are key contributors to chronic diseases such as obesity, diabetes, and cardiovascular conditions. This study employs machine learning (ML) techniques to analyze dietary intake, identify patterns, and assess their relationships with health outcomes. The aim is to provide personalized dietary recommendations and insights to promote healthier eating habits. Data for this research were sourced from a Kaggle dataset on foods and nutrients and the National Health and Nutrition Examination Survey (NHANES) on health outcomes. Preprocessing steps included data cleaning, feature selection, and transformation using one-hot encoding and scaling techniques. Machine learning algorithms were applied to build a food recommendation system and a diet health check system. Visualizations such as correlation heatmaps, scatter plots, and dashboards further illustrated the relationships between demographic factors, nutrient intake, and health outcomes. The food recommendation system effectively identified foods with similar nutritional profiles to user preferences. For instance, it suggested nutrient-rich alternatives like rice noodles and kale, achieving similarity scores above 0.99 in multiple test cases. The diet health check system analyzed nutrient intake against predefined thresholds and provided tailored recommendations. Excessive carbohydrate, protein, fat, and cholesterol consumption were linked to conditions such as diabetes, coronary heart disease, and cancer, with specific dietary adjustments suggested for improvement. This study demonstrates the power of machine learning in personalizing dietary advice and enhancing health outcomes. By leveraging advanced algorithms and diverse datasets, the developed systems present a scalable solution for promoting balanced diets and mitigating chronic disease risks. Further refinement and broader implementation of these tools are recommended to maximize their impact on public health.
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Harwalkar, Mukund, Sneha Jadhav, Sakshi Birdar, and Sanjeevini Joshi. "VITAMIN DEFICIENCY AND FOOD RECOMMENDATION USING MACHINE LEARNING." International Journal of Engineering Applied Sciences and Technology 7, no. 5 (2022): 95–98. http://dx.doi.org/10.33564/ijeast.2022.v07i05.016.
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study by WHO reports that inadequate and imbalanced intake of food causes around 9% of heart attack deaths, about 11% of ischemic heart disease deaths, and 14% of gastrointestinal cancer deaths worldwide. Moreover, around billions children are suffering from different types deficiency from Vitamin-A to vitamin k deficiency, 0.2 billion people are suffering from iron deficiency (anaemia), and 0.7 billion people are suffering from iodine deficiency. The main objective of this work to recommend a diet to different individual. The recommender system deals with a large volume of information present from the dataset.
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Gajalakshmi N, Andalpriya C, Raja Lakshmi K, and Nuttrenai V. "Fit AI-Personalized Diet and Fitness Planner." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 03 (2025): 508–13. https://doi.org/10.47392/irjaem.2025.0080.
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Maintaining a healthy lifestyle is becoming increasingly challenging due to hectic schedules, unhealthy eating habits, and the lack of personalized diet and fitness guidance. Generic health plans often fail to address individual requirements, leading to ineffective results and poor adherence. To overcome these challenges, FitAI: Personalized Diet and Fitness Planner is developed as an AI-powered web application that provides customized diet and fitness recommendations based on user-specific data. The system collects key user inputs, including age, height, weight, gender, exercise frequency, dietary preferences, and existing health conditions, to generate tailored health plans. By leveraging machine learning algorithms, FitAI analyzes this data to offer dynamic and adaptive suggestions that evolve based on user progress. Unlike static diet charts or generic fitness apps, FitAI continuously refines recommendations to match changing user needs. The application is developed using Python, Streamlit, and Mediapipe, ensuring a seamless, interactive, and intelligent user experience. Streamlit provides an intuitive web interface, simplifying user interactions, while Mediapipe enables real-time fitness tracking, ensuring correct posture and exercise execution. The AI-driven recommendation system continuously learns from user habits, improving the accuracy and relevance of health suggestions. The system was evaluated based on accuracy, efficiency, and user satisfaction, demonstrating a 90% alignment with expert health plans and 85% positive user feedback. The Mediapipe-based posture tracking effectively improved exercise form, while the Streamlit interface enhanced accessibility and engagement. However, challenges such as tracking accuracy variations and limited real-time health monitoring indicate areas for future improvement. FitAI bridges the gap between generic health recommendations and personalized wellness solutions, empowering individuals to take control of their fitness and nutrition. By integrating machine learning, adaptive AI models, and user-friendly web technologies, FitAI presents a smart, data-driven solution for individuals seeking effective and sustainable health management. Future enhancements may include integration with wearable devices and advanced deep learning models for more precise and real-time health tracking.
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Uduakobong, M. Umoren, A. Ojokoh Bolanle, and O. S. Ijarotimi. "Addressing Malnutrition in School-Aged Children with a Diet Recommender System." International Journal of Innovative Science and Research Technology 7, no. 9 (2022): 1704–18. https://doi.org/10.5281/zenodo.7223141.
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Automated recommender systems have been developed to make up for human inadequacies in decision making while solving the information overload problem. They have also found profound use in the area of diet and nutrition. Nevertheless, child nutrition in recommender systems is yet under researched, with very few works found in this area. This research work employs a switching hybrid recommendation technique that is a combination of user-based collaborative filtering and human expert knowledge for both healthy and malnourished children; to cater for the nutrition needs of children on a large and much improved scale while being accessible and available to children, parents and caregivers in different locations at the same time. Six elementary schools in Nigeria were visited for data gathering on children food interests, likes and dislikes. Open ended and dichotomous questions were used to obtain vital information for the system; and these responses are incorporated as initial user and food database to check the cold-start problem. Waterlows’ classification model was used to profile and classify users into their health classes and user-based collaborative filtering algorithm was used to recommend meals to the users based on user-user similarity. Human expert knowledge built from interaction with nutritionist was incorporated into the system and used in the recommendation process for both healthy and malnourished children. System evaluation results show the overall optimal performance and acceptance of the system. The results of this work can be adopted to reduce the scourge of malnutrition in children through healthy diet provisioning, especially in the Nigerian context.
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Mrs. Nirupa V, Gagguturi Afree, Pathan Ashraf Khan, Marannagari Harini, and Salladhi Jaswanth Kumar. "Automated Detection and Recommendation System for Parkinson’s Disease Using Machine Learning." International Research Journal of Innovations in Engineering and Technology 09, Special Issue ICCIS (2025): 150–54. https://doi.org/10.47001/irjiet/2025.iccis-202524.
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Abstract - Parkinson’s Disease (PD) is a chronic neurological condition that significantly affects speech and motor control. Early diagnosis plays a vital role in symptom management and slowing disease progression. This project presents an automated machine learningbased system for early detection and severity classification of Parkinson’s Disease using voice signal features. Key voice measurements such as jitter, shimmer, and harmonic-to-noise ratio are extracted from biomedical voice data to train multiple classifiers. An ensemble model combining XGBoost, K-Nearest Neighbors, Decision Tree, and Gaussian Naive Bayes achieves high diagnostic accuracy. The system also incorporates severity prediction (Mild, Moderate, Severe) based on probability scores and provides personalized recommendations related to exercise, diet, and therapy. The best-performing model is deployed in a Flask-based web application, enabling users to input voice features and receive real-time feedback. This non-invasive, cost- effective, and user-friendly system aids in clinical diagnosis, enhances early detection, and empowers patients with actionable health insights
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Eniforo Kingsley Orevaoghene * , Chika Yinka-Banjo and Emmanuel John Anagu. "Fuzzy Logic Based Personalised Diet Recommendation Engine for Dietary Prevention and Control of Diabetics." International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence 4, no. 1 (2025): 13. https://doi.org/10.54938/ijemdcsai.2025.04.1.429.
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Dietary management is a cornerstone in the prevention and treatment of Type 2 diabetes mellitus. Despite advancements in understanding dietary approaches, many patients rely on generalized advice rather than individualized plans. The affordability and accessibility of nutritious food remain significant barriers in low- and middle-income settings. Additionally, there is limited integration of technology-based tools into routine care to assist healthcare providers in delivering personalized dietary recommendations. The aim of this research is to develop a personalized food mapping system that aligns dietary recommendations with the health conditions and preferences of diabetes patients. The system derives its framework from the Nigerian food composition table thus providing culturally appropriate advice to its users. Analysing nutritional values together with local food glycemic indexes enables users to identify more suitable dietary choices which both match their nutritional requirements and food tastes. An individualised dietary system helps users maintain their planned meals more easily and these strategies work to preserve blood sugar levels. By providing recommendations that are truly individualized, it can help people better manage their blood sugar and overall health, which is essential for preventing serious long-term complications. Beyond diabetes, this personalized diet system could serve as a model for managing other health conditions. It offers a more effective way to give dietary advice, improving patient outcomes and making healthcare more efficient. Ultimately, this approach could make it easier for healthcare providers and patients alike to manage diet-related health challenges in a way that feels more personalized and adaptable.