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Journal articles on the topic 'Machine learning in healthcare'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Vasundhara, S. "Challenges of Machine Learning in Healthcare Industry." International Journal of Science and Research (IJSR) 12, no. 7 (2023): 495–97. http://dx.doi.org/10.21275/sr23706105157.

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Narayan Koranchirath, Nithin. "Impact of Machine Learning on Healthcare Analytics." International Journal of Science and Research (IJSR) 13, no. 2 (2024): 942–47. http://dx.doi.org/10.21275/sr24210203022.

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Winter, George. "Machine learning in healthcare." British Journal of Healthcare Management 25, no. 2 (2019): 100–101. http://dx.doi.org/10.12968/bjhc.2019.25.2.100.

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Haritima, Haritima, S. Sakena Benazer, Tatiraju V. Rajani Kanth, and K. Dhineshkumar. "An Adaptive Learning-Driven Software Ecosystem for Optimized Healthcare Solutions with Artificial Intelligence." International Journal of BIM and Engineering Science 09, no. 2 (2024): 45–54. http://dx.doi.org/10.54216/ijbes.090206.

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The use of machine learning methods in healthcare has shown encouraging outcomes in terms of better patient care, more efficient use of resources, and streamlined operations. Traditional machine learning methods encounter difficulties when dealing with healthcare data due to its complexity and heterogeneity. Healthcare applications are a good fit for Gradient Boosting Machines (GBMs), which have become a formidable tool for structured data and predictive modelling jobs. Better healthcare system capabilities, including more precise forecasts and well-informed decisions, may be achieved by the i

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Kambala, Mahesh. "AI-Powered Healthcare: Transforming Patient Outcomes with Machine Learning." Journal of Medical Science and clinical Research 12, no. 08 (2024): 34–47. http://dx.doi.org/10.18535/jmscr/v12i08.07.

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AI and ML have flooded the healthcare industry with new technological approaches to affect patient experiences through smart approaches towards predictability, treatment, and diagnosis. The following paper focuses on exploring the effects caused by the implementation of artificial intelligence technologies in the sphere of healthcare. This research explores different case studies to prove that early diagnosis, treatment customization, and organizational effectiveness are all driven by AI. The paper is concerned with the approaches used in the implementation of artificial intelligence in the he

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Chen, Irene Y., Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, and Marzyeh Ghassemi. "Ethical Machine Learning in Healthcare." Annual Review of Biomedical Data Science 4, no. 1 (2021): 123–44. http://dx.doi.org/10.1146/annurev-biodatasci-092820-114757.

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The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges.

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Siddique, Sarkar, and James C. L. Chow. "Machine Learning in Healthcare Communication." Encyclopedia 1, no. 1 (2021): 220–39. http://dx.doi.org/10.3390/encyclopedia1010021.

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Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare c

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Chen, Irene Y., Shalmali Joshi, Marzyeh Ghassemi, and Rajesh Ranganath. "Probabilistic Machine Learning for Healthcare." Annual Review of Biomedical Data Science 4, no. 1 (2021): 393–415. http://dx.doi.org/10.1146/annurev-biodatasci-092820-033938.

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Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial, including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.

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Arora, Aaryan, and Nirmalya Basu. "Machine Learning in Modern Healthcare." International Journal of Advanced Medical Sciences and Technology 3, no. 4 (2023): 12–18. http://dx.doi.org/10.54105/ijamst.d3037.063423.

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Traditional healthcare systems have long struggled to meet the diverse needs of millions of patients, leading to inefficiencies and suboptimal outcomes. However, the advent of machine learning (ML) has introduced a transformative paradigm shift towards value-based treatment, enabling healthcare providers to deliver personalized and highly effective care. Modern healthcare equipment and devices now incorporate internal applications that gather and store comprehensive patient data, presenting a valuable resource for ML-driven predictive models. In this research article, we delve into the profoun

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Aaryan, Arora, and Basu Nirmalya. "Machine Learning in Modern Healthcare." International Journal of Advanced Medical Sciences and Technology (IJAMST) 3, no. 4 (2023): 12–18. https://doi.org/10.54105/ijamst.D3037.063423.

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<strong>Abstract: </strong>Traditional healthcare systems have long struggled to meet the diverse needs of millions of patients, leading to inefficiencies and suboptimal outcomes. However, the advent of machine learning (ML) has introduced a transformative paradigm shift towards value-based treatment, enabling healthcare providers to deliver personalized and highly effective care.Modern healthcare equipment and devices now incorporate internal applications that gather and store comprehensive patient data, presenting a valuable resource for ML-driven predictive models. In this research article,

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PUROHIT, ABHISHEK, and YUVRAJ KARARWAL. "Machine Learning in Healthcare System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40687.

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Machine learning (ML) is transforming healthcare by improving diagnosis accuracy, customizing treatment regimens, and reducing administrative procedures. Its capacity to analyze large datasets and detect complex patterns has resulted in major improvements in patient care and operational efficiency. In diagnostics, ML algorithms have shown extraordinary skill in analyzing medical pictures such as X-rays and MRIs, frequently outperforming humans. For example, ML models in radiology may detect abnormalities with amazing precision, aiding early illness identification and increasing patient outcome

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Machine learning algorithms for healthcare. "Machine learning algorithms for healthcare." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 1139–43. https://doi.org/10.30574/wjarr.2025.25.2.0308.

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Machine learning technology has led to major changes in how healthcare works. Machine learning tools bring new ways to improve every part of healthcare delivery from detecting conditions to planning treatments and checking patients. New data gathering methods alongside stronger computer systems and smarter programs make ML systems more valuable for medical use. This publication studies how machine learning programs help healthcare systems solve complicated health problems. Our investigation shows how ML algorithms recognize medical conditions including cancer, diabetes, and heart diseases whil

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Reena Dhan, Archana, and Binod Kumar. "Machine Learning for Healthcare: Predictive Analytics and Personalized Medicine." International Journal of Science and Research (IJSR) 13, no. 6 (2024): 1307–13. http://dx.doi.org/10.21275/mr24608013906.

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Roopa, K. "Evaluating Fairness in Healthcare Machine Learning: A Quantitative Approach." International Journal of Science and Research (IJSR) 12, no. 8 (2023): 2270–74. http://dx.doi.org/10.21275/sr23820234214.

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Kondoro, A., and E.F. Tongora. "SMART HEALTHCARE SYSTEM FOR CARDIOVASCULAR PATIENTS USING MACHINE LEARNING." POLISH JOURNAL OF SCIENCE, no. 52 (July 13, 2022): 22–28. https://doi.org/10.5281/zenodo.6825811.

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Cardiovascular refers to anything relating to the heart and blood vessels. The flawed current leads to multiple patients not receiving the right care depending on their current health status, leading to critical cases and even death. Apart from the main objective of developing a smart healthcare system for cardiovascular diseases, other specific objectives include modelling a machine learning algorithm for natural language processing, modelling a machine learning algorithm for health recommendation, developing a preliminary diagnosis subsystem, developing a follow-up report management subsyste

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Taware, Rasika, Awais Khan, Huzaifa Khan, Ayush Jogi, and Prof. (Dr) Ranjit Keole. "Machine Learning Based, Healthcare Recommendation System." International Journal of Ingenious Research, Invention and Development (IJIRID) 4, no. 2 (2025): 320–27. https://doi.org/10.5281/zenodo.15249384.

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<em>Access to good healthcare shouldn’t depend on where you live or how much you know, it should just be there when you need it. But for so many people, especially in remote or underserved areas, even basic medical help feels out of reach [1]. That’s what led us to build a simple, human,first system that listens to what someone’s going through, whether typed or spoken, and helps them make sense of it [2], [3]. You tell it your symptoms in your own words, and it offers a possible explanation for what might be happening with your health [4]. No hospital visit. No complicated pr

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Kaur Walia, Rupinder. "Empowering Healthcare with Machine Learning: A Comprehensive Review of Machine Learning-based COVID-19 Detection, Diagnosis and Treatment." Journal of Advanced Research in Medical Science & Technology 10, no. 3&4 (2023): 06–08. http://dx.doi.org/10.24321/2394.6539.202303.

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Yuvraj, Singh, Singh Parth, Pratap Singh Dhirender, Pratap Singh Yash, Natasha Sharma Er., and Tanuj. "Unlocking Healthcare Insights: Disease Prediction with Machine Learning." Unlocking Healthcare Insights: Disease Prediction with Machine Learning 8, no. 11 (2023): 6. https://doi.org/10.5281/zenodo.10167542.

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This research paper explores the utilization of Machine Learning (ML) techniques in disease prediction, specifically targeting diabetes, heart disease and lung cancer. As healthcare increasingly adopts data-driven decision-making through advanced data analysis and predictive modeling, our study employs established ML algorithms - Logistic Regression, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) - to accurately predict these diseases. Our primary aim is to showcase the efficacy of these algorithms, facilitating timely intervention and improved patient care by health

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Penikalapati, Pragathi, and A. Nagaraja Rao. "Healthcare analytics by engaging machine learning." Science in Information Technology Letters 1, no. 1 (2020): 24–39. http://dx.doi.org/10.31763/sitech.v1i1.32.

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M, Mrs Adithi, ,. Pavan Kumar R, Priya Y. S, Sneha B. S, and Vaishnavi O. "Smart Healthcare Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem39440.

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In this paper, the utilization of machine learning techniques in the healthcare system is introduced. As the healthcare industry generates increasingly vast amounts of data daily, manual processing by humans becomes impractical for prompt disease diagnosis and treatment decisions. To address this challenge, data management techniques and machine learning algorithms are explored in healthcare applications to facilitate more accurate decision-making processes. Detailed descriptions of medical data are provided, enhancing various facets of healthcare applications through the adoption of this cutt

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Ugale, Prof Archana, Abhijeet Gadakh, Aniket Malunjkar, Roshan Sawant, and Vaibhav Dhakane. "Integrated Healthcare System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1121–23. http://dx.doi.org/10.22214/ijraset.2022.47520.

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Abstract: In this digital world, data is an asset, and enormous data was generated in all fields. Data in the healthcare industry consists of all the information related to patients. Here a general architecture has been proposed for predicting disease in the healthcare industry. Many of the existing models are concentrating on one disease per analysis. There is no common system present that can analyze more than one disease at a time. Thus, we are concentrating on providing immediate and accurate disease predictions to the users about the symptoms they enter along with the disease predicted. S

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Pal, Debrupa, Animesh Ghosh, Sourav Majumdar, Ashadeep Pan, and Debmitra Ghosh. "Machine Learning in Healthcare: A Review." International Journal of Darshan Institute on Engineering Research and Emerging Technologies 12, no. 1 (2023): 61–67. http://dx.doi.org/10.32692/ijdi-eret/12.1.2023.2309.

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Ugale, Prof Archana. "Integrated Healthcare System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4008–17. http://dx.doi.org/10.22214/ijraset.2023.52536.

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Abstract: In this digital world, data is an asset, and enormous data was generated in all fields. Data in the healthcare industry consists of all the information related to patients. Here a general architecture has been proposed for predicting disease in the healthcare industry. Many of the existing models are concentrating on one disease per analysis. There is no common system present that can analyse more than one disease at a time

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24

Ramesh, Banoth, G. Srinivas, P. Ram Praneeth Reddy, M. D. Huraib Rasool, Divya Rawat, and Madhulita Sundaray. "Feasible Prediction of Multiple Diseases using Machine Learning." E3S Web of Conferences 430 (2023): 01051. http://dx.doi.org/10.1051/e3sconf/202343001051.

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Automated Multiple Disease Prediction System using Machine Learning is an advanced healthcare application that utilizes machine learning algorithms to accurately predict the likelihood of a patient having multiple diseases based on their medical history and symptoms. The system employs a comprehensive dataset of medical records and symptoms of various diseases, which are then analysed using machine learning techniques such as decision trees, support vector machines, and random forests. The system’s predictions are highly accurate, and it can assist medical professionals in making more informed

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Ayush, Mittal, Kumar Vijay, Jha Abhishek, Khanna Bhavuk, and Jayesh. "Identifying Suspicious Activities in Medical Insurance Claims Using Machine Learning." Journal of Advances in Computational Intelligence Theory 6, no. 1 (2023): 15–24. https://doi.org/10.5281/zenodo.10223997.

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<i>The research methodology involves collecting and preprocessing a comprehensive dataset comprising healthcare claims and associated fraud labels. Multiple machine learning algorithms, including logistic regression, decision trees, random forests, support vector machines, and neural networks, are implemented and evaluated. Performance metrics such as accuracy, precision, recall, and F1 score are used to assess the effectiveness of each model.</i><i>The results of the study demonstrate that machine learning techniques exhibit considerable potential in healthcare fraud detection. The comparativ

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Bhavishya, Katta. "Incorporating Machine Learning into an IoT-Based Healthcare System." European Journal of Advances in Engineering and Technology 9, no. 5 (2022): 149–56. https://doi.org/10.5281/zenodo.14274616.

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In recent years, there has been a surge of interest in studying the IoT among academics. In its most basic form, it is the linking of various electronic devices together through the internet. It is important to highlight some of the most effective uses of IoT technology in the domains of healthcare monitoring, in addition to its more general usage in relation to smart homes and autonomous vehicles. Providing excellent services for patients is the primary goal of our study work. The use of this tool can raise the bar for home health care, making it safer for patients and facilitating the delive

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Biswas, Dr Sudarsan. "Explainable AI in Healthcare: Enhancing Trust through Interpretable Machine Learning Models." International Journal of Machine Learning, AI & Data Science Evolution 1, no. 01 (2025): 22–31. https://doi.org/10.63665/ijmlaidse.v1i1.03.

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As artificial intelligence continues to reshape the healthcare industry, a growing concern among professionals and patients is the "black-box" nature of many machine learning models. While accuracy remains important, trust in AI decisions is equally vital, especially in critical areas like diagnosis and treatment planning. This paper explores the role of Explainable Artificial Intelligence (XAI) in building that trust by making machine learning outputs more transparent and understandable. Using real-world datasets and a case study in cardiovascular disease prediction, we evaluate how interpret

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Aiswariya Milan, K., and Niharika P. Kumar. "Machine Learning Techniques in Healthcare—A Survey." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4276–79. http://dx.doi.org/10.1166/jctn.2020.9061.

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The development of science and technology has led to a very busy lifestyle among urban people across the globe. Due to the advent of cutting-edge technologies, connectivity and networking is a boon to the people living in urban areas. Thus, a vast amount of patient data from admission, treatment and discharge is collected across the clinical community. These rich data being available online has been under-utilized and the question arises on how best the data can be utilized. With the centralized data and powerful data analytical algorithms are running in powerful machines, until recent past, t

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Kleczyk, Ewa J. "Peercite Journal of Artificial Intelligence & Machine Learning." Peercite Journal of Artificial Intelligence & Machine Learning 2, no. 1 (2024): 2018–29. http://dx.doi.org/10.61641/pjaiml.2024.2.s1.

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The integration of Artificial Intelligence (AI) into healthcare has brought significant advancements in diagnostics, treatment, and patient care, but it also raises critical ethical concerns. This article examines the new landscape shaped by AI in healthcare, focusing on the balance between innovation and ethical considerations such as privacy, data security, and algorithmic bias. It highlights the importance of interdisciplinary collaboration among healthcare professionals, ethicists, technologists, and policymakers to address these challenges. By ensuring transparency, accountability, and eq

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Researcher. "ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING: THE INFLUENCE OF MACHINE LEARNING ON PREDICTIVE ANALYTICS IN HEALTHCARE." International Journal of Machine Learning and Cybernetics (IJMLC) 2, no. 2 (2024): 1–13. https://doi.org/10.5281/zenodo.13321533.

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Artificial Intelligence (AI) and Machine Learning (ML) have transformed numerous industries, including healthcare. Predictive analytics, a subset of data analytics that employs ML algorithms to examine current and historical data for forecasting future events, has become vital in healthcare for enhancing patient outcomes, lowering costs, and optimizing resource management. This paper examines the influence of machine learning on predictive analytics in healthcare, highlighting its applications, advantages, challenges, and future prospects.

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B.S, Mr Akshay Nayak. "Predictive Medicine Recommendation System Powered by Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50712.

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Abstract - The Predictive Medicine Recommendation System Powered by Machine learning (ML), a system where intelligently suggests suitable medications based on user-provided inputs, using machine learning techniques to enhance accuracy and relevance symptoms or medical conditions. By integrating advanced classification algorithms such as Support Vector Machines (SVM), Random Forest, and Naive Bayes, the system aims to reduce prescription errors, enhance diagnostic efficiency, and support personalized healthcare delivery. The platform supports early disease prediction, medication advice, and ris

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Manikkannan, D. "PredictiCare: Empowering Health Predictions with Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27450.

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Thanks to the rapid development of machine learning and data analysis techniques, new applications are no w possible in the healthcare sector. The program prov ides disease prediction that uses patient data and the p ower of machine learning algorithms to predict the oc currence of specific diseases. The system is designed to help healthcare providers make decisions, provide t imely treatment and improve patient outcomes. The s ystem incorporates many types of machine learning, i ncluding classification techniques such as random for ests, support vector machines, and neural networks. T hese mo

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Twum, Samuel. "A Machine Learning for Phishing Detection in Healthcare." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49275.

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ABSTRACT—Phishing, a social Engineering attacks are becoming more common because of the quick digitization of healthcare services. Phishing is often used to steal user data, including login credentials and credit card numbers. It occurs when an attacker, masquerading as a trusted entity, dupes a victim into opening an email, instant message, or text message and this, occurring at the healthcare services clearly puts patient data security and system integrity at danger. This study introduces a brand-new machine learning-based method for identifying phishing attempts directed at healthcare insti

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Ibanga, Diana-Abasi, and Sara Peppe. "The Missing Link of Machine Learning in Healthcare." Balkan Journal of Philosophy 14, no. 1 (2022): 11–22. http://dx.doi.org/10.5840/bjp20221413.

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The aim of this article is to show how the ambivalent nature of reality might impact artificial intelligence (AI) use in medicine. The work illustrates that machine learning (ML) modelling requires some significant levels of data straight-jacketing to be efficient. However, data objectification will be counter-productive in the long run in AI-enabled medical contexts. The problem is that the ambivalent nature of realities requires a non-objectified modelling process, which is missing in machine learning at the moment. On the basis of this, the study hypothesizes that AI-enabled medicine will c

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Animesh, Kumar, and Dr Srikanth V. "Enhancing Healthcare through Human-Robot Interaction using AI and Machine Learning." International Journal of Research Publication and Reviews 5, no. 3 (2024): 184–90. http://dx.doi.org/10.55248/gengpi.5.0324.0831.

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Sravani, V. "Enhanced Healthcare Data Security Through MI-Based Cyberattack Detection in SDN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42953.

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It is critical to protect sensitive data against hackers in the healthcare industry. Although they are used for effective resource management and security, software-defined networks, or SDNs, are susceptible to many types of assaults. A machine learning-based cyberattack detector (MCAD) designed specifically for healthcare systems is presented in this research. Enhancing network security, the system uses an adapted layer three (L3) learning switch application to collect and analyze normal and anomalous traffic, and then deploys MCAD on the Ryu controller. Many machine learning algorithms, such

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Grote, Thomas, and Philipp Berens. "On the ethics of algorithmic decision-making in healthcare." Journal of Medical Ethics 46, no. 3 (2019): 205–11. http://dx.doi.org/10.1136/medethics-2019-105586.

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In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might imp

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Kang, Rachael, Esa M. Rantanen, and Eric A. Youngstrom. "Machine Learning in Healthcare: Two Case Studies." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (2022): 774–78. http://dx.doi.org/10.1177/1071181322661518.

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Machine learning (ML) is making significant inroads into the field of medicine. It can be used as a preventative measure by predicting a patient’s diagnosis and introducing early treatment to prevent adverse outcomes or lessen their impact. However, despite many demonstrated advantages of machine learning tools in health-care, their performance assessment remains partial at best. In particular, human interactions with machine learning tools in clinical settings remain poorly researched. This review examined machine learning tools in two important areas, sepsis diagnosis and suicide prediction.

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Saleem, Tausifa Jan, and Mohammad Ahsan Chishti. "Exploring the Applications of Machine Learning in Healthcare." International Journal of Sensors, Wireless Communications and Control 10, no. 4 (2020): 458–72. http://dx.doi.org/10.2174/2210327910666191220103417.

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The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment

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Anjum, Uzma. "Artificial Intelligence, Machine Learning and Deep Learning In Healthcare." Bioscience Biotechnology Research Communications 14, no. 7 (2021): 144–48. http://dx.doi.org/10.21786/bbrc/14.7.36.

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Saxena, Ms Kavita, Rishabh Sharma, Rishav Kumar, and Roshan Kumar. "Disease Prediction Using Machine Learning and Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2655–63. http://dx.doi.org/10.22214/ijraset.2022.42871.

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Abstract: Health being the state of complete physical and mental wellbeing is an imperative part of humankind .Healthcare sector been a capital incentive sector having complicated entry barrier for investors like acquiring land for making hospital, stamp duties on it, human resource crunch which further act as roadblock for the government in providing universal good healthcare services to its citizenry . In this regard artificial intelligence is leading to disruption in the healthcare sector which is helping poor in safeguarding them from been exploited by extravagant out of pocket expenditure

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Prakash, Ujjwal. "Advanced Dietitian Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33347.

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The "Advanced Artificial Intelligence Dietitian" (AI-Dietitian) project explores the potential of AI to revolutionize personalized nutrition. Traditional dietary approaches often struggle with individual complexities and lack continuous guidance. We propose an AI-powered system that transcends these limitations, offering: · Comprehensive Data Integration: The AI-Dietitian analyzes diverse data sets, including medical records, genetic information, activity trackers, and food intake sensors, building a holistic understanding of individual needs and preferences. · Adaptive & Personalized Plan

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Mahajan, Tejasvini Govinda. "Multiple Diseases Prediction Using Machine Learning." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04002.

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Abstract - The Multiple Diseases Prediction Using Machine Learning project intends to become that intelligent and efficient system that will predict the chances of getting various diseases from the health data shared by the user with the algorithm. Multiple models like Decision Trees, Random Forests, Support Vector Machines, and Neural Networks are trained on extremely large medical datasets containing symptoms, demographics, and clinical measurements. After preprocessing the data to deal with missing values, normalize the inputs, and carry out feature selection, the systems then learn to asso

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Desai, Ms Shruti, and Mr Rahul Erulan. "Medical Chatbot in Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2101–3. http://dx.doi.org/10.22214/ijraset.2023.50502.

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Abstract: This article suggests a medical chatbot that uses machine learning methods to provide personalised healthcare assistance. The chatbot is intended to interact with patients, collect information about their symptoms, medical history, and other pertinent details, and give treatment guidance and recommendations. Natural language processing (NLP) algorithms are used in the suggested chatbot to comprehend patients' requests and provide pertinent answers. It also analyses medical documents and other data using machine learning algorithms to provide personalised health suggestions. To provid

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Sarker, Mithun. "Reinventing Wellness: How Machine Learning Transforms Healthcare." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, no. 1 (2024): 116–31. http://dx.doi.org/10.60087/jaigs.v3i1.73.

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Traditional healthcare systems have long grappled with meeting the diverse needs of millions of patients, often resulting in inefficiencies and suboptimal outcomes. However, the emergence of machine learning (ML) has brought about a transformative shift towards value-based treatment, empowering healthcare providers to deliver personalized and highly effective care. Today's healthcare equipment and devices are equipped with internal applications that collect and store comprehensive patient data, serving as a rich resource for ML-driven predictive models. This research delves into the profound i

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Sumathi, P., Arun Kumar S, and Balaji A. "Healthcare - Autism Predicting Tool Using Data Science / AI / ML." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 440–43. http://dx.doi.org/10.22214/ijraset.2024.60421.

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Abstract: This study presents a comprehensive analysis of the application of machine learning techniques for the prediction of autism spectrum disorder (ASD). The dataset used in this research comprises a range of demographic, behavioral, and diagnostic features. The study focuses on the use of various machine learning algorithms, including limited decision trees, support vector machines, and neural networks, to predict the likelihood of ASD in individuals. In addition, engineering and feature selection strategies are investigated to determine the most pertinent characteristics for precise pre

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Mittal, Prakhar, Udit Parasher, Ruhi Khanna, Parv Yadav, Sunil Kumar, and Mariya Khurshid. "A Machine Learning-based Healthcare Diagnostic Model." International Journal of Computer Applications 184, no. 16 (2022): 1–4. http://dx.doi.org/10.5120/ijca2022922147.

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Mittal, Prakhar, Udit Parasher, Ruhi Khanna, Parv Yadav, Sunil Kumar, and Mariya Khurshid. "A Machine Learning based Healthcare Diagnostic Model." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3195–99. http://dx.doi.org/10.22214/ijraset.2022.42985.

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Abstract: This To live a healthy life, healthcare is considered very important but it is terribly difficult to get consultation from a doctor nowadays. The basic idea and motive of the project is to build a chatbot system by using AI (Artificial Intelligence) that can predict the health problems and give details of the disease before consulting the Doctor. The system provides text assistance to coordinate with the chatbot. Based on user’s symptoms chatbot will provide what kind of disease user have and provide doctor’s details according to the diagnose. Based on the symptom that user have chat

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Zhang, Kaiyi, Jianwu Wang, Tianyi Liu, Yifei Luo, Xian Jun Loh, and Xiaodong Chen. "Machine Learning‐Reinforced Noninvasive Biosensors for Healthcare." Advanced Healthcare Materials 10, no. 17 (2021): 2100734. http://dx.doi.org/10.1002/adhm.202100734.

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Ozaydin, Bunyamin, Eta S. Berner, and James J. Cimino. "Appropriate use of machine learning in healthcare." Intelligence-Based Medicine 5 (2021): 100041. http://dx.doi.org/10.1016/j.ibmed.2021.100041.

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