Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comprehensive evaluation and performance analysis of machine learning in heart disease prediction Subject The topic of the resource Healthcare; Heart disease; Machine learning; Prediction Description An account of the resource Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the models robustness. Techniques like the under sampling clustering oversampling method (USCOM) address the issue of data imbalance, thereby improving the models predictive capabilities. The classification challenge employs a multilayer deep convolutional neural network (MLDCNN), trained using the adaptive elephant herd optimization method (AEHOM). The proposed machine learning-based heart disease prediction method (ML-HDPM) demonstrates outstanding performance across various crucial evaluation parameters, as indicated by its comprehensive assessment. During the training process, the ML-HDPM model exhibits a high level of performance, achieving an accuracy rate of 95.5% and a precision rate of 94.8%. The systems sensitivity (recall) performs with a high accuracy rate of 96.2%, while the F-score highlights its well-balanced performance, measuring 91.5%. It is worth noting that the specificity of ML-HDPM is recorded at a remarkable 89.7%. The findings underscore the potential of ML-HDPM to transform the prediction of heart disease and aid healthcare practitioners in providing precise diagnoses, exerting a substantial influence on patient care outcomes. The Author(s) 2024. Creator An entity primarily responsible for making the resource Al-Alshaikh H.A.; Prabu P.; Poonia R.C.; Saudagar A.K.J.; Yadav M.; AlSagri H.S.; AlSanad A.A. Source A related resource from which the described resource is derived Scientific Reports, Vol-14, No. 1 Publisher An entity responsible for making the resource available Nature Research Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1038/s41598-024-58489-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1038/s41598-024-58489-7</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189828590&amp;doi=10.1038%2Fs41598-024-58489-7&amp;partnerID=40&amp;md5=eaa09e3089ac399fd4255b8a04179494" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189828590&amp;doi=10.1038%2fs41598-024-58489-7&amp;partnerID=40&amp;md5=eaa09e3089ac399fd4255b8a04179494</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 20452322; PubMed ID: 38570582 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Al-Alshaikh H.A., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Prabu P., Department of Computer Science, CHRIST University, Bangalore, 560029, India; Poonia R.C., Department of Computer Science, CHRIST University, Bangalore, 560029, India; Saudagar A.K.J., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Yadav M., Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, India; AlSagri H.S., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; AlSanad A.A., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia