{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T22:14:35Z","timestamp":1775859275653,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T00:00:00Z","timestamp":1687219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Prince Sultan University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Heart disease is a significant global health issue, contributing to high morbidity and mortality rates. Early and accurate heart disease prediction is crucial for effectively preventing and managing the condition. However, this remains a challenging task to achieve. This study proposes a machine learning model that leverages various preprocessing steps, hyperparameter optimization techniques, and ensemble learning algorithms to predict heart disease. To evaluate the performance of our model, we merged three datasets from Kaggle that have similar features, creating a comprehensive dataset for analysis. By employing the extra tree classifier, normalizing the data, utilizing grid search cross-validation (CV) for hyperparameter optimization, and splitting the dataset with an 80:20 ratio for training and testing, our proposed approach achieved an impressive accuracy of 98.15%. These findings demonstrated the potential of our model for accurately predicting the presence or absence of heart disease. Such accurate predictions could significantly aid in early prevention, detection, and treatment, ultimately reducing the mortality and morbidity associated with heart disease.<\/jats:p>","DOI":"10.3390\/a16060308","type":"journal-article","created":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T02:01:33Z","timestamp":1687312893000},"page":"308","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":95,"title":["Enhancing Heart Disease Prediction through Ensemble Learning Techniques with Hyperparameter Optimization"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1535-7944","authenticated-orcid":false,"given":"Daniyal","family":"Asif","sequence":"first","affiliation":[{"name":"Department of Mathematics, COMSATS University Islamabad, Park Road, Islamabad 45550, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9208-7091","authenticated-orcid":false,"given":"Mairaj","family":"Bibi","sequence":"additional","affiliation":[{"name":"Department of Mathematics, COMSATS University Islamabad, Park Road, Islamabad 45550, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6009-5609","authenticated-orcid":false,"given":"Muhammad Shoaib","family":"Arif","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia"},{"name":"Department of Mathematics, Air University, PAF Complex E-9, Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8798-3297","authenticated-orcid":false,"given":"Aiman","family":"Mukheimer","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,20]]},"reference":[{"key":"ref_1","unstructured":"Bonow, R.O., Mann, D.L., Zipes, D.P., and Libby, P. 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