Log in

Relevant bibliographies by topics / Early detection of heart disease / Journal articles

To see the other types of publications on this topic, follow the link: Early detection of heart disease.

Journal articles on the topic 'Early detection of heart disease'

Author: Grafiati

Published: 12 May 2023

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Early detection of heart disease.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Xaitov, Farxod Nasriddin o'g'li., and Zilola Mo'min qizi. Axmatjonova. "EARLY DETECTION AND DIAGNOSIS OF HEART DISEASES." Multidisciplinary Journal of Science and Technology 5, no. 1 (2025): 33–37. https://doi.org/10.5281/zenodo.14621680.

Full text

Abstract:

The article discusses factors that influence the development of the disease, such as unhealthy lifestyle, stress, genetic factors and aspects related to chronic diseases. Early detection and diagnosis of heart disease. Heart disease is the leading cause of death and disability worldwide. This article discusses the importance of early detection and diagnosis of heart disease. Early detection of heart disease can save patients' lives and prevent complications. The article focuses on the early signs of heart disease, risk factors, and diagnostic methods, including modern methods such as electroca

APA, Harvard, Vancouver, ISO, and other styles

2

Dr., Deepak NR, Prasad Ashansh, BE Harsh, B. Shurthi, Moond Himanshu, and Sharma Chekit. "Machine Learning for Early Detection of Heart Disease." Journal of Research and Review: Hacking Techniques and Information Security Systems 1, no. 1 (2025): 33–38. https://doi.org/10.5281/zenodo.15195374.

Full text

Abstract:

<em>Heart disease continues to be a leading cause of death globally, with early detection playing a critical role in improving outcomes and preventing severe complications. Traditional diagnostic methods, such as electrocardiograms (ECGs), echocardiograms, and stress tests, often detect conditions at later stages, when intervention becomes more challenging. This report explores the role of machine learning in early heart disease detection, highlighting various ML techniques such as supervised learning, deep learning, and unsupervised learning. These models can identify patterns and correlation

APA, Harvard, Vancouver, ISO, and other styles

3

Sobeih, Abul Ahmed. "HEART HEALTH CHECK: SCREENING UNIVERSITY STUDENTS FOR RHEUMATIC HEART DISEASE." International Journal of Prevention Practice and Research 04, no. 03 (2024): 01–07. http://dx.doi.org/10.55640/medscience-abcd634.

Full text

Abstract:

This study examines the feasibility and effectiveness of screening university students for Rheumatic Heart Disease (RHD) as part of a broader public health initiative. RHD remains a significant cause of morbidity and mortality, particularly in resource-limited settings, despite being largely preventable. The screening program targets university students due to their age group's susceptibility to RHD and the potential to intervene early in the disease progression. Through a combination of clinical assessments, echocardiography, and laboratory tests, students are evaluated for signs and symptoms

APA, Harvard, Vancouver, ISO, and other styles

4

Aleem, Anuoluwapo Oluwayemisi. "MACHINE LEARNING FOR EARLY HEART DISEASE DETECTION." International Research Journal of Modernization in Engineering Technology and Science 06, no. 09/September-2024 (2024): 1–14. https://doi.org/10.56726/IRJMETS61879.

Full text

Abstract:

Heart disease has been proven to be the leading cause of death for both men and women, killing about 697,000 people in the US in 2020, which makes it a major concern to be dealt with. Identifying heart disease in patients could be quite challenging due to several contributory risk factors, which requires some high level of techniques. Machine learning proves to be effective in predicting heart disease in patients given the contributory risk factors. In this study, we used seven different machine learning algorithms such as K-Nearest Neighbors, support vector machine, D

APA, Harvard, Vancouver, ISO, and other styles

5

Zabeeulla, M., C. Sharma, and A. Anand. "Early Detection of Heart Disease Using Machine Learning Approach." CARDIOMETRY, no. 26 (March 1, 2023): 342–47. http://dx.doi.org/10.18137/cardiometry.2023.26.342347.

Full text

Abstract:

Heart attack is one of the leading causes of morbidity in the worldwide population. Cardiovascular disease is one of the major diseases involved in clinical data analysis or one of the most important part for forecasting. Early detection of cardiovascular diseases can help to reduce high-risk condition for heart patients to make individual decisions for their lifestyle adjustments, mitigating the challenges. Early detection of heart disease has been explored in this study using a machine-learning approach. Additionally, we used sampling strategies to deal with disparate datasets. The overall r

APA, Harvard, Vancouver, ISO, and other styles

6

Wonderful, Ntepa. "Heart disease detection system." i-manager's Journal on Data Science & Big Data Analytics 2, no. 1 (2024): 1. http://dx.doi.org/10.26634/jds.2.1.20688.

Full text

Abstract:

This paper presents a comprehensive exploration into the utilization of machine learning (ML) techniques to revolutionize medical diagnostics, with a specific focus on enhancing the detection of heart disease. Recognizing the imperative need for early diagnosis to address the global prevalence of heart disease, this study delves into the development and application of advanced ML principles. The paper aims to construct a robust ML model capable of analyzing diverse patient data sets, including electronic health records and genetic information, to discern intricate patterns and correlations imp

APA, Harvard, Vancouver, ISO, and other styles

7

Kumar, Prof Amit, Harshika Bansal, Ayush Jaiswal, and Sovit Kumar Gupta. "Early Disease Prediction using Ml." International Journal of Advanced Engineering and Nano Technology 10, no. 11 (2023): 1–4. http://dx.doi.org/10.35940/ijaent.i9694.11101123.

Full text

Abstract:

The approach employed in disease prediction using machine learning involves making forecasts about various diseases by utilizing symptoms provided by patients or other individuals. The supervised machine learning approaches called random forest classifier, KNN classifier, SVMs classifier are employed to forecast the disease. These algorithms are used to determine the disease's probability. Accurate medical data analysis helps with patient care and early disease identification as biomedical and healthcare data volumes rise. Diabetes, heart diseases are just a few of the illnesses we can forecas

APA, Harvard, Vancouver, ISO, and other styles

8

Dr., Chandrasekar Vadivelraju, N. Sughanditha Reddy Duttala, Praneeth Kumar Gowd Korrapati, Vaahini Katakaraju, and Suresh Pujari. "Heart Disease Prediction." Heart Disease Prediction 9, no. 1 (2024): 6. https://doi.org/10.5281/zenodo.10527852.

Full text

Abstract:

The increasing breakthroughs in illness diagnosis classification and identification systems have led to a steady growth in the incorporation of machine learning in medical diagnostics. These systems provide crucial data aiding medical professionals in the early detection of fatal diseases, significantly enhancing patient survival rates. Globally, heart disease stands as the leading cause of death. The escalating rates of heart strokes among juveniles underscore the need for an early detection system to prevent potential incidents. Frequent and costly tests like electrocardiograms (ECG) are imp

APA, Harvard, Vancouver, ISO, and other styles

9

Singh, Ankita, and Nupur Soni. "Detection of Cardiovascular Disease Using AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–5. https://doi.org/10.55041/ijsrem40092.

Full text

Abstract:

Detecting illnesses in their early stages can help prevent serious complications and improve treatment outcomes. Early diagnosis is critical because it reduces the risk of severe consequences, as prevention is better than cure. A high death rate often occurs when diseases are not detected early. Expert systems can bridge this gap by diagnosing diseases automatically in their initial phases. These systems use fuzzy, rule-based engines to analyze patient data and apply forward-chaining techniques for diagnosis. In this study, data such as age, gender, blood sugar levels, blood pressure, and ECG

APA, Harvard, Vancouver, ISO, and other styles

10

Manzar, Shabih. "Fetal Atrial Flutter: Early Detection and Treatment." Obstetrics Gynecology and Reproductive Sciences 7, no. 5 (2023): 01–03. http://dx.doi.org/10.31579/2578-8965/176.

Full text

Abstract:

Fetal atrial flutter (AF) can occur with structurally normal hearts or congenital heart disease, including atrioventricular septal defect, hypoplastic left heart syndrome, pulmonary atresia, and Ebstein's malformation. 1 It is important to detect fetal AF early and treat it appropriately. We present a case of fetal AF that was treated timely, resulting in no associated complications. The infant was born by cesarean section at a gestational age of 37 weeks with an Apgar score of 9 and 9. The birth weight was 2860 grams. The antenatal history was positive for fetal tachycardia.

APA, Harvard, Vancouver, ISO, and other styles

11

Ramanathan V, Aniruth, Sriram Yerra, Swetha N G, and Pralipth Gandikota. "Advancements in Heart Disease Prediction: A Machine Learning Approach for Early Detection and Risk Assessment." International Journal of Science and Research (IJSR) 13, no. 10 (2024): 774–80. http://dx.doi.org/10.21275/sr241004171836.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Subhashini, Nallanagula, M.Dilip Kumar, G.Y.S Sai Karthik, M. Nagaraju, and V. Sai Kishore. "Heart Check: Predictive Analytics for Early Heart Disease Detection using Machine Learning." Journal of Nonlinear Analysis and Optimization 15, no. 02 (2024): 899–905. https://doi.org/10.36893/jnao.2024.v15i2.141.

Full text

Abstract:

Heart diseases or cardiovascular diseases refer to a variety of conditions that affect your heart health like the blood vessel problem, irregular heartbeat rhythms as well as congenital heart defects. Being a mentioned top cause of death worldwide has rendered early, accurate diagnosis more crucial than before in order to be able provide proper management and control. That is what thisresearch, HeartCheck will be aiming for; automation of predicting heart disease using a machine learning model that can help the healthcare provider to make decisions at right time. The system will perform data p

APA, Harvard, Vancouver, ISO, and other styles

13

Whalley, Gillian. "Appropriate and early detection of rheumatic heart disease." Australasian Journal of Ultrasound in Medicine 23, no. 1 (2020): 3–4. http://dx.doi.org/10.1002/ajum.12203.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Al-Habahbeh, Mohammad, Moath Alomari, Hebatullah Khattab, et al. "Heart disease detection using machine learning." Bulletin of Electrical Engineering and Informatics 14, no. 2 (2025): 1151–59. https://doi.org/10.11591/eei.v14i2.8324.

Full text

Abstract:

Heart disease continues to be a major worldwide health issue, requiring accurate prediction models to improve early identification and treatment. This research aims to address two main objectives in light of the increasing prevalence of heart-related disorders. Firstly, it aims to determine the most efficient classifier for identifying heart disease among twenty-nine different classifiers that represent six distinct learning strategies. Furthermore, the research seeks to identify the most effective method for selecting features in heart disease datasets. The results show how well different cla

APA, Harvard, Vancouver, ISO, and other styles

15

Ahmed, Abul. "EXAMINING UNIVERSITY STUDENTS FOR THE POSSIBILITY OF DEVELOPING RHEUMATOID HEART DISEASE." International Journal of Public Health and Awareness 06, no. 01 (2023): 01–07. http://dx.doi.org/10.55640/ijpha-611.

Full text

Abstract:

As part of a larger public health campaign, this study investigates the viability and efficacy of screening university students for rheumatic heart disease (RHD). Even while RHD is mostly preventable, it nevertheless contributes significantly to morbidity and mortality, especially in environments with limited resources. University students are the focus of the screening program because of their age group's heightened vulnerability to RHD and the opportunity to intervene early in the disease's course. Students are assessed for signs and symptoms of RHD using a combination of laboratory tests, c

APA, Harvard, Vancouver, ISO, and other styles

16

A, Dr Arudra. "DETECTA – MULTIPLE DISEASE DETECTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31416.

Full text

Abstract:

Machine Learning Techniques for Predictive Analytics in Healthcare There are multiple techniques in machine learning that can in a variety of industries, do predictive analytics on large amounts of data. Predictive analytics in healthcare is a difficult endeavor, but it can eventually assist practitioners in making timely decisions regarding patients' health and treatment based on massive data. Diseases like Breast cancer, diabetes, and heart-related diseases are causing many deaths globally but most of these deaths are due to the lack of timely check-ups of the diseases. The above problem occ

APA, Harvard, Vancouver, ISO, and other styles

17

Mishra, Pawan, Nitesh Singh, Sudhanshu Ranjan, and Vishal Patel. "Automated Cardiovascular Disease Detection with Hybrid Machine Approach." International Journal of Innovative Research in Advanced Engineering 11, no. 11 (2024): 834–40. https://doi.org/10.26562/ijirae.2024.v1111.07.

Full text

Abstract:

Cardiovascular ailment (CVD) is a main reason of death and disability globally, posing tremendous challenges for early detection and prevention. A heart condition or the accumulation of fatty deposits inside the arteries, is frequently linked to CVD and raises the risk of blood clots and other consequences. Predicting and diagnosing CVD is critical to mitigating its effect and enhancing patient results. With the appearance of massive data in healthcare, large volumes of patient records are available that may assist in identifying early warning symptoms of coronary heart disorder. This project

APA, Harvard, Vancouver, ISO, and other styles

18

Devi, G. Rama. "Automated Detection of Rheumatic Heart Disease via Unsegmented Heart Sound Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28743.

Full text

Abstract:

Rheumatic Heart Disease (RHD) remains a significant cause of morbidity and mortality in many parts of the world, particularly in low-resource settings. Early detection and intervention are critical to mitigating its progression and reducing associated complications. This paper presents an automated approach for the detection of RHD utilizing unsegmented heart sound analysis through deep learning techniques. By leveraging Spectro-temporal representations of raw heart sound signals, our proposed method aims to capture subtle yet discriminative patterns indicative of RHD pathology. The deep learn

APA, Harvard, Vancouver, ISO, and other styles

19

Anika and Navpreet Kaur. "A Review on Heart Disease Detection Techniques." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (2017): 395. http://dx.doi.org/10.23956/ijarcsse/v7i7/0200.

Full text

Abstract:

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely

APA, Harvard, Vancouver, ISO, and other styles

20

Catharina Sagita Moniaga, Jasmine Syabania Noviantri, Giovanno Sebastian Yogie, Yohanes Firmansyah, and Hendsun Hendsun. "Kegiatan Pengabdian Masyarakat dalam Edukasi Penyakit Dislipidemia serta Komplikasinya terhadap Penyakit Kardiovaskular." Jurnal Kabar Masyarakat 1, no. 2 (2023): 20–30. http://dx.doi.org/10.54066/jkb-itb.v1i2.310.

Full text

Abstract:

Coronary heart disease occurs when the blood vessels that supply blood to the heart are blocked by plaque build up in the walls of the blood vessels. Uncontrolled dyslipidemia can lead to faster plaque build up and a higher risk of having a heart attack. Community service activities are important to increase public awareness of dyslipidemia and coronary heart disease for early detection before complications occur. Community service activities were carried out at the Kalam Kudus II Jakarta Foundation involving 52 respondents. Activities carried out in the form of community education, physical a

APA, Harvard, Vancouver, ISO, and other styles

21

Rosli, Daniyal, Mazura Mat Din, and Zanariah Idrus. "Heartcare: Predictive Analytics for Early Detection and Prevention." IC-ITECHS 5, no. 1 (2024): 323–31. https://doi.org/10.32664/ic-itechs.v5i1.1672.

Full text

Abstract:

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, often due to late detection and prevention. Heartcare aims to leverage predictive analytics to facilitate early detection and prevention of heart diseases. By integrating machine learning algorithms such as Decision Trees, Random Forests, and Logistic Regression, Heartcare provides healthcare professionals with a powerful tool for patient health monitoring. This study focuses on developing a predictive model to assess heart disease risk using patient-specific data, such as age, sex, BMI, and lifestyle factors. The

APA, Harvard, Vancouver, ISO, and other styles

22

Catur Andryani, S.Si., MSc., Dr Nur Afny, Muhamad Femy Mulya, Surnanto Surnanto, and M. Rizam Kusfandi. "Rancang Bangun Purwarupa Aplikasi Deteksi Dini Penyakit Jantung Berbasis Case Base Reasoning dengan Keamanan Data." Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) 5, no. 1 (2021): 66–73. http://dx.doi.org/10.47970/siskom-kb.v5i1.230.

Full text

Abstract:

The prevalence of heart disease has been consistently increasing in five recent years. In average 15 out of 1000 people have heart disease. Currently heart disease becomes the second leading cause of death in Indonesia. Early detection will guide the appropriate treatments to increase recovery opportunity. In another hand, many healthcare facilities in Indonesia are not equipped with the cardiologist. It triggers many heart disease cases are late to handle due late detection. Thus, we propose web based early heart disease detection application prototype using Case Base Reasoning framework. It

APA, Harvard, Vancouver, ISO, and other styles

23

Bujade, Om. "A Robust Predictive Model for Early Detection of Heart Disease using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29655.

Full text

Abstract:

Heart failure is a chronic disease affecting millions worldwide. An efficient machine learning- based technique is needed to predict heart failure health status early and take necessary actions to overcome this worldwide issue. While medication is the primary treatment, exercise is increasingly recognized as an effective adjunct therapy in managing heart failure. This research project aims to develop a robust predictive model for the early detection of heart diseases by leveraging machine learning techniques, with a specific focus on the application of Support Vector Machines (SVM). The growth

APA, Harvard, Vancouver, ISO, and other styles

24

Anilkumar, Reshma, and P. Vineetha Sankar. "Machine Learning for Personalized Risk Assessment and Early Detection of Heart Disease." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 3207–18. http://dx.doi.org/10.22214/ijraset.2023.51828.

Full text

Abstract:

Abstract: Today in our busy life it very difficult for us to look after our health. Cardio vascular diseases are very common now days which cause loss of billions of lives world-wide. Our lifestyles have a major impact on our health causing various chronic diseases. Machine learning (ML) can revolutionize the field of cardiovascular disease prediction by providing more accurate, nuanced, and personalized risk assessment, leading to improved patient health. A properly trained machine learning model can easily detect or predict the heart disease. Within the context of using artificial intelligen

APA, Harvard, Vancouver, ISO, and other styles

25

Parbate, Pranay, Dhairyashil Thombare, Tushar Yerkal, Adarsh Varpe, and Prof Suresh Reddy. "Multiple Disease Detection System." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 997–1003. http://dx.doi.org/10.22214/ijraset.2024.59966.

Full text

Abstract:

Abstract: In the realm of healthcare, the early detection of multiple diseases presents a formidable challenge but holds immense potential for improving patient outcomes. This paper proposes an integrative approach for the simultaneous detection of four prevalent diseases: heart disease, Parkinson's disease, diabetes, and skin cancer. Leveraging advanced machine learning techniques, our framework encompasses data preprocessing methods for cleaning and normalization, feature selection strategies to extract discriminative features from heterogeneous medical datasets, and ensemble classification

APA, Harvard, Vancouver, ISO, and other styles

26

Kandukuri, Kumar, and A. Sandhya. "Heart Stroke Detection Using KNN Algorithm." ECS Transactions 107, no. 1 (2022): 18385–93. http://dx.doi.org/10.1149/10701.18385ecst.

Full text

Abstract:

Diagnosis of heart diseases have been improved in recent days with the help of machine learning (ML). The early prediction of heart disease is possible by analyzing the important parameters with the help of data mining techniques. In this study, K-Nearest Neighborhood (KNN) is used for the classification of heart stroke with parameter weighting methods to improve accuracy and 11 parameters were identified for training the KNN algorithm. The result shows that the accuracy using the KNN algorithm (11 parameters) is more efficient to predict the early heart stroke detection. This proposed algorit

APA, Harvard, Vancouver, ISO, and other styles

27

Hara, Akira, Masayuki Niwa, Tomohiro Kanayama, et al. "Galectin-3: A Potential Prognostic and Diagnostic Marker for Heart Disease and Detection of Early Stage Pathology." Biomolecules 10, no. 9 (2020): 1277. http://dx.doi.org/10.3390/biom10091277.

Full text

Abstract:

The use of molecular biomarkers for the early detection of heart disease, before their onset of symptoms, is an attractive novel approach. Ideal molecular biomarkers, those that are both sensitive and specific to heart disease, are likely to provide a much earlier diagnosis, thereby providing better treatment outcomes. Galectin-3 is expressed by various immune cells, including mast cells, histiocytes and macrophages, and plays an important role in diverse physiological functions. Since galectin-3 is readily expressed on the cell surface, and is readily secreted by injured and inflammatory cell

APA, Harvard, Vancouver, ISO, and other styles

28

Najmu, Nissa, Jamwal Sanjay, and Mohamma Shahid. "Early Detection of Cardiovascular Disease using Machine learning Techniques an Experimental Study." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 3 (2020): 635–41. https://doi.org/10.35940/ijrte.C46570.99320.

Full text

Abstract:

Human body prioritizes the heart as the second most important organ after the brain. Any disruption in the heart ultimately leads to disruption of the entire body. Being the members of modern era, enormous changes are happening to us on a daily basis that impact our lives in one way or the other. A major disease among top five fatal diseases includes the heart disease which has been consuming lives worldwide. Therefore, the prediction of this disease is of prime importance as it will enable one to take a proper and needful approach at a proper time. Data mining and machine learning are taking

APA, Harvard, Vancouver, ISO, and other styles

29

Vaidya, Ayush. "Predictive Modeling for Heart Diseases Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 560–63. https://doi.org/10.22214/ijraset.2025.70225.

Full text

Abstract:

In this Research paper focuses on the development and application of predictive modeling techniques for the early detection of heart disease. Heart disease remains a leading cause of death globally, making early diagnosis and prevention essential. This project seeks to develop a reliable system for predicting the risk of heart disease by utilizing modern machine learning and data analysis techniques, drawing on patient data such as demographics, lifestyle habits, medical background, and clinical test results. By applying various predictive algorithms, such as decision trees, support vector mac

APA, Harvard, Vancouver, ISO, and other styles

30

Dr. Saravanan G, Brettlee Salomon N, Cibi S, Dharun T, and Hari Krishnan P. "AI- Based Heart Disease Detection Using Machine Learning." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 902–7. https://doi.org/10.47392/irjaeh.2025.0129.

Full text

Abstract:

Heart disease continues to be among the top causes of death globally, and early and precise diagnosis is required for proper treatment. Advances in artificial intelligence (AI) and machine learning (ML) in recent times have made it possible to create predictive models that can aid in early detection and risk prediction of heart disease. This work suggests a machine learning method of heart disease detection from patient health information, with clinical factors such as blood pressure, cholesterol levels, and lifestyle. A range of ML algorithms, from logistic regression and decision trees to su

APA, Harvard, Vancouver, ISO, and other styles

31

Nagavelli, Umarani, Debabrata Samanta, and Partha Chakraborty. "Machine Learning Technology-Based Heart Disease Detection Models." Journal of Healthcare Engineering 2022 (February 27, 2022): 1–9. http://dx.doi.org/10.1155/2022/7351061.

Full text

Abstract:

At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Naïve Bayes with a weighted approach is used for predicting heart disease. The second one, a

APA, Harvard, Vancouver, ISO, and other styles

32

Khan, Mahammad Sahil, and Asst Prof Archana Panda. "Heart Disease Prediction Using Machine Learning Algorithms." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–11. http://dx.doi.org/10.55041/ijsrem36570.

Full text

Abstract:

Heart disease is a major issue that has become increasingly prevalent. According to current statistics, heart disease claims the life of one person every minute. In the last several years, one of the hardest problems facing the medical field is predicting heart disease. Reducing the death rate can be achieved with early detection of cardiac disease. Machine learning is the most effective approach to forecasting heart disease. This paper aims to create a lightweight, straightforward solution to detecting cardiac disease using machine learning. Machine learning can aid in heart disease predictio

APA, Harvard, Vancouver, ISO, and other styles

33

Ramya, Prof T. "Heart disease Prediction and detection using Machine Learning Algorithms." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27400.

Full text

Abstract:

This paper focuses on building a heart disease detection system using machine learning. A Python- based application is developed for healthcare research, offering reliability and versatility. The process involves handling categorical data, collecting databases, KNN, and attribute evaluation. A KNN is introduced for improved accuracy, around 96.58%, in identifying heart diseases. The algorithm's experiments and outcomes are discussed, highlighting enhanced research diagnosis accuracies. The paper concludes by summarizing objectives, limitations, and research contributions . Cardiovascular disea

APA, Harvard, Vancouver, ISO, and other styles

34

Kiran, Londhe, and Kadam Aakash. "Heart Diseases Detection using Machine Learning Algorithms." International Journal of Innovative Science and Research Technology 8, no. 3 (2023): 2161–64. https://doi.org/10.5281/zenodo.7811138.

Full text

Abstract:

Predicting and detection of heart disease has always been a critical and challenging task for healthcare practitioners. Hospitals and other clinics are offering expensive therapies and operations to treat heart diseases. S o, predicting heart disease at the early stages will be useful to the people around the world so that they will take necessary actions before getting severe. Heart disease is a significant problem in recent times; the main reason for this disease is the intake of alcohol, tobacco, and lack of physical exercise. Over the years, machine learning shows effective results in maki

APA, Harvard, Vancouver, ISO, and other styles

35

Sylvester Aondonenge, Dugguh, Ajayi Ore-Ofe, Kamorudeen Hassan Taiwo, et al. "Early Heart Disease Prediction Using Data Mining Techniques." Vokasi Unesa Bulletin of Engineering, Technology and Applied Science 2, no. 2 (2025): 211–26. https://doi.org/10.26740/vubeta.v2i2.36735.

Full text

Abstract:

This study develops a predictive model for early heart disease detection using data mining techniques to enhance timely and accurate diagnosis. Heart disease prediction is complex due to the need to analyze various risk factors, such as age, cholesterol, and blood pressure. The model integrates multiple machines learning algorithms, including Random Forest, Support Vector Machine, and a hybrid ensemble approach, aiming to achieve higher prediction accuracy and reliability. The methodology follows five phases which include data collection, data pre-processing, feature extraction, model construc

APA, Harvard, Vancouver, ISO, and other styles

36

Hanok Hruday Mohan, Yekula, Peddaguravagari Thejaswi, and Vanajakshamma. "School Health Screening For Early Detection Of Obesity, Congenital Heart Disease And Rheumatic Heart Disease." Indian Heart Journal 74 (November 2022): S10—S11. http://dx.doi.org/10.1016/j.ihj.2022.10.164.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Amit Jain, Suresh Babu Dongala, and Aruna Kama. "Heart disease prediction using machine learning techniques." Open Access Research Journal of Engineering and Technology 3, no. 1 (2022): 001–6. http://dx.doi.org/10.53022/oarjet.2022.3.1.0028.

Full text

Abstract:

Heart diseases are commonly caused and when neglected becomes life threatening. So, early detection of the disease is very important and for diagnosis to save lives. There can be many parameters that are to be considered to predict the heart disease. Some of them are like age, cholesterol, blood pressure levels. Etc., here we are going to implement Machine Learning model to predict heart disease.

APA, Harvard, Vancouver, ISO, and other styles

38

Kalyan, Bamane, Singh Manjeet, Singh Divyanshu, Patil Ruchita, and Nikumbh Vishakha. "Disease Detection using AI and IoT." Recent Trends in Information Technology and its Application 4, no. 2 (2021): 1–10. https://doi.org/10.5281/zenodo.4979971.

Full text

Abstract:

Now a days health monitoring has become a crucial part of human lifestyle. Machine learning models can help us in predicting the disease beforehand for us and help us to fight it efficiently. Taking the capability of IOT technology into account, it is possible to overcome the difficulties. A raspberry pi powered device which will take the help of various ML algorithm to help us identify heart disease, diabetes disorder, skin disease. This device will take the help of various sensors like heart rate sensor, ECG sensor, camera module etc. to reduce the human intervention and give accurate input

APA, Harvard, Vancouver, ISO, and other styles

39

Raja, Dinesh, Mohammed Fateen, Mahi Teja Talluri, and V. Karthikeyan. "A Review towards Sustainable Heart Rate-Based Disease Detection: Prioritizing Environmental Impact, Access, and Health Outcomes." IOP Conference Series: Earth and Environmental Science 1375, no. 1 (2024): 012008. http://dx.doi.org/10.1088/1755-1315/1375/1/012008.

Full text

Abstract:

Abstract Sustainability in heart rate-based disease detection goes beyond diagnostics, embracing innovation that considers environmental impact, access, and health outcomes. Prioritizing energy efficiency and eco-friendly materials reduces the devices’ carbon footprint. Secure data management ensures privacy while optimizing resource use. Equitable access bridges gaps, reaching all communities for better health outcomes. Long-term benefits arise from preventive measures and healthier lifestyles. Adherence to regulations ensures safety and environmental responsibility. Education fosters informe

APA, Harvard, Vancouver, ISO, and other styles

40

Yadav, Shivani. "Heart Disease Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–14. http://dx.doi.org/10.55041/ijsrem36858.

Full text

Abstract:

Heart disease remains a leading cause of mortality worldwide, necessitating improved methods for early detection and risk assessment. This paper reviews and analyzes the application of machine learning techniques in heart disease prediction, focusing on five primary algorithms: Naïve Bayes, k-Nearest Neighbor (KNN), Decision Tree, Artificial Neural Network (ANN), and Random Forest. By examining existing studies and datasets, we evaluate the effectiveness of these algorithms in predicting heart disease risk. Our analysis demonstrates that machine learning models can significantly enhance the ac

APA, Harvard, Vancouver, ISO, and other styles

41

Hartge, David R., Jan Weichert, Martin Krapp, Ute Germer, Ulrich Gembruch, and Roland Axt-Fliedner. "Results of early foetal echocardiography and cumulative detection rate of congenital heart disease." Cardiology in the Young 21, no. 5 (2011): 505–17. http://dx.doi.org/10.1017/s1047951111000345.

Full text

Abstract:

AbstractObjectiveThe aim of this study is to evaluate the cumulative detection rate of foetal echocardiography during gestation and in the early neonatal period, with a special emphasis on early foetal echocardiography.MethodsWe conducted a retrospective survey of all singleton pregnancies from 1993 to 2007, with complete sequential echocardiography from 11 plus 0 to 13 plus 6 weeks of gestation. It was mandatory to have at least one foetal echocardiography in the second or third trimester and one postnatally.ResultsOur study included 3521 pregnancies, in which 77 cases were diagnosed with con

APA, Harvard, Vancouver, ISO, and other styles

42

M L, Sinchana. "Heart Disease Prediction using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49823.

Full text

Abstract:

Abstract - With the rise in cardiovascular diseases (CVDs), machine learning (ML) has become a key tool in early diagnosis using clinical data. Studies utilize algorithms like SVM, Random Forest, ANN, Decision Trees, and ensemble models, stressing the importance of data preprocessing and feature selection (e.g., PCA, RFE) for improved accuracy. Hybrid and ensemble techniques enhance predictive performance, while model interpretability remains vital for clinical use. Overall, ML models, when properly trained and tuned, significantly aid in the early detection of heart disease and support timely

APA, Harvard, Vancouver, ISO, and other styles

43

Kanwal, Amna, Khawaja Tehseen Ahmad, Kamran Abid, and Naeem Aslam. "Detection of Heart Disease Using Supervised Machine Learning." VFAST Transactions on Software Engineering 10, no. 3 (2022): 58–70. http://dx.doi.org/10.21015/vtse.v10i3.1106.

Full text

Abstract:

One of the most prevailing and serious disease affecting human’s health is Heart Disease (HD). Early diagnosis may allow for heart disease prevention or reduction, which could lower the rate of death.Machine Learning techniques have produced a variety of solutions for heart disease prediction and is capable of predicting illness at early stage . This study propose a model that includes many machine learning (ML) techniques to obtain accurate heart disease (HD) predictions. Data collection and pre-processing are used to create accurate data for the training model. Supervised Machine learning cl

APA, Harvard, Vancouver, ISO, and other styles

44

Karan Potdukhe. "Heart Disease Prediction and Detection Using Machine Learning." Journal of Information Systems Engineering and Management 10, no. 20s (2025): 373–81. https://doi.org/10.52783/jisem.v10i20s.3130.

Full text

Abstract:

The critical need for efficient early-stage identification is underscored by the fact that cardiovascular diseases (CVDs) continue to rank as the top cause of mortality worldwide. It is very difficult for doctors to make an accurate diagnosis of early-stage cardiac disease. The good news is that there are now viable options for rapid diagnosis and treatment thanks to developments in current diagnostic technology. The overarching goal of this study is to draw connections between the capabilities of ML and DL algorithms for cardiovascular health data analysis. Improving the precision and consist

APA, Harvard, Vancouver, ISO, and other styles

45

Muhammad, Yar, Moteeb Almoteri, Hana Mujlid, et al. "An ML-Enabled Internet of Things Framework for Early Detection of Heart Disease." BioMed Research International 2022 (September 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/3372296.

Full text

Abstract:

Healthcare occupies a central role in sustainable societies and has an undeniable impact on the well-being of individuals. However, over the years, various diseases have adversely affected the growth and sustainability of these societies. Among them, heart disease is escalating rapidly in both economically settled and undeveloped nations and leads to fatalities around the globe. To reduce the death ratio caused by this disease, there is a need for a framework to continuously monitor a patient’s heart status, essentially doing early detection and prediction of heart disease. This paper proposes

APA, Harvard, Vancouver, ISO, and other styles

46

Solomatin, V. A., and O. G. Baybuz. "Application of the efficientnet model for detecting ischemic heart disease." System technologies 1, no. 156 (2025): 160–65. https://doi.org/10.34185/1562-9945-1-156-2025-18.

Full text

Abstract:

This article examines a neural network model that facilitates the early detection of is-chemic heart disease on chest X-rays, playing a crucial role in the diagnosis and treatment of cardiovascular diseases. The study explores the application of convolutional neural net-works(CNNs) for the automatic detection of ischemic heart disease on chest X-rays. A dataset containing chest X-rays annotated by an experienced cardiologist was used in the study. A CNN model was developed and trained to detect signs of ischemic heart disease based on chest X-rays. The model was trained on a small dataset (700

APA, Harvard, Vancouver, ISO, and other styles

47

Tahir, Naquash H. B., Jorson Pheba, Khanum Ruqsana, Hepzibah Ruth, and Niveditha S. "Review on Chatbot to Predict Heart Disease using Machine Learning Algorithms." Research and Reviews: Advancement in Cyber Security 1, no. 2 (2024): 34–42. https://doi.org/10.5281/zenodo.10948583.

Full text

Abstract:

<em>A chatbot that is developed for the detection of Heart Disease plays an important role in the medical field as it provides the accessible and immediate preliminary assessments. It conducts the screening of individuals efficiently for present or potential heart issues. It also identifies the risk factors associated with the particular condition, contributing to a comprehensive health awareness. Other than delivering early warnings it also offers online monitoring and emergency assistance creating a proactive approach to heart health. Integration with health records enables continuous data c

APA, Harvard, Vancouver, ISO, and other styles

48

Wiharto, Wiharto, and Nada Mufidah Farah. "Early detection of coronary heart disease based on risk factors using interpretable machine learning." International Journal of Advances in Applied Sciences (IJAAS) 13, no. 4 (2024): 944–56. https://doi.org/10.11591/ijaas.v13.i4.pp944-956.

Full text

Abstract:

Coronary heart disease (CHD) is the leading cause of death in the world. The risk of coronary heart disease can be reduced or even prevented by early detection. Early detection of CHD has been widely developed using machine learning, but the machine learning algorithms used sometimes have low interpretability. Low interpretability makes it difficult for users to understand the cause of the decision. Referring to this, this research aims to propose an early detection model using machine learning interpretability, which is implemented using the C5.0 algorithm and interpreted using Shapley additi

APA, Harvard, Vancouver, ISO, and other styles

49

Proshanta Kumar Bhowmik, Mohammed Nazmul Islam Miah, Md Kafil Uddin, et al. "Advancing Heart Disease Prediction through Machine Learning: Techniques and Insights for Improved Cardiovascular Health." British Journal of Nursing Studies 4, no. 2 (2024): 35–50. http://dx.doi.org/10.32996/bjns.2024.4.2.5.

Full text

Abstract:

Heart disease persists as one of the leading causes of death in the USA and worldwide, accounting for a substantial proportion of global mortality. The significance of early detection of heart disease lies in its capability to counter catastrophic events such as strokes and heart attacks, which are often irreversible and fatal. Machine learning algorithms are gradually revolutionizing heart disease prediction since they can handle complex, multi-dimensional data sets. This research project used the Cleveland dataset from the UCI Machine Learning Repository, containing 70,000 records of patient

APA, Harvard, Vancouver, ISO, and other styles

50

Yadav, Shivani, Avdhesh Yadav, Dr Shobhit Srivastava, and Piyush Rai. "Heart Disease Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–4. http://dx.doi.org/10.55041/ijsrem37304.

Full text

Abstract:

Heart disease continues to be a major global cause of death, highlighting the urgent need for more effective methods of early detection and risk evaluation. This paper explores the use of machine learning techniques for predicting heart disease, focusing on five key algorithms: Naïve Bayes, k-Nearest Neighbor (KNN), Decision Tree, Artificial Neural Network (ANN), and Random Forest. Through a comprehensive review of existing research and data, we assess the performance of these algorithms in heart disease risk prediction. The analysis reveals that machine learning approaches offer substantial i

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!