{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T01:35:54Z","timestamp":1777685754826,"version":"3.51.4"},"reference-count":37,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Hybrid Intelligent Systems"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:p>Heart diseases are a major cause of death worldwide, highlighting the need for early detection. The electrocardiogram (ECG) records the heart\u2019s electrical activity using electrodes. Our research focuses on the ECG data to diagnose heart disorders, particularly arrhythmias. We utilized the MIT-BIH arrhythmia dataset for comparative analysis of various machine learning techniques, including random forest, K-Nearest Neighbor, and Decision Tree, along with deep learning algorithms like Long short-term memory and Convolutional Neural Networks. This required employing various preprocessing methods like filtering and normalization and feature selection techniques such as chi-square and sequential feature selectors to improve the performance of heart disease prediction. Therefore, hybrid machine and deep learning models are proposed, and the results reveal that hybrid models perform better than conventional models.<\/jats:p>","DOI":"10.3233\/his-240017","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T11:46:59Z","timestamp":1720180019000},"page":"14-28","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparative analysis of machine learning algorithms for heart disease prediction"],"prefix":"10.1177","volume":"21","author":[{"given":"Isha","family":"Gupta","sequence":"first","affiliation":[{"name":"Thapar Institute of Engineering and Technology, Patiala, India"}]},{"given":"Anu","family":"Bajaj","sequence":"additional","affiliation":[{"name":"Thapar Institute of Engineering and Technology, Patiala, India"}]},{"given":"Vikas","family":"Sharma","sequence":"additional","affiliation":[{"name":"Thapar Institute of Engineering and Technology, Patiala, India"}]}],"member":"179","published-online":{"date-parts":[[2025,1,23]]},"reference":[{"key":"bibr1-HIS-240017","doi-asserted-by":"publisher","DOI":"10.3390\/info13100475"},{"key":"bibr2-HIS-240017","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3191669"},{"key":"bibr3-HIS-240017","doi-asserted-by":"crossref","unstructured":"Itoo N.N., Garg V.K., Heart Disease Prediction using a Stacked Ensemble of Supervised Machine Learning Classifiers, In 2022 International Mobile and Embedded Technology Conference, pp.\u00a0599\u2013604.","DOI":"10.1109\/MECON53876.2022.9751883"},{"key":"bibr4-HIS-240017","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3289584"},{"key":"bibr5-HIS-240017","doi-asserted-by":"crossref","unstructured":"Vijaya J., Heart Disease Prediction using Clustered Genetic Optimization Algorithm, In 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, 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