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 Conference Papers Conference Paper Faculty Publications- Conference Papers 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 Assessing and Exploring Machine Learning Techniques for Cardiovascular Disease Prediction using Cleveland and Framingham Datasets Subject The topic of the resource cardiovascular disease (CVD); Cleveland Dataset; Decision Tree (DT); Framingham Dataset; HD (HD); Logistic Regression (LR); Machine Learning (ML); Random Forest (RF); Support Vector Machine (SVM) Description An account of the resource Heart disease prediction using machine learning has garnered significant attention due to its potential for early diagnosis and intervention. This study presents an analysis of various machine learning algorithms applied to HD prediction across multiple research papers. The goal of this study is to analyze the performance and predictive capabilities of various machine learning algorithms in predicting heart disease across different datasets and research papers. Algorithms such as Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, Naive Bayes, and Gradient Boosting were evaluated using diverse datasets and parameters. In the Cleveland dataset, both Random Forest and Decision Tree classifiers achieved perfect accuracy 100%. Conversely, in the Framingham dataset, Random Forest exhibited the highest accuracy at 94%, followed by SVM at 87.45%, and Decision Tree at 85.23%. While specific algorithm performance varies depending on the dataset and parameters considered, ensemble methods like Random Forest often demonstrate superior performance. These findings underscore the effectiveness of machine learning in HD prediction and emphasize the significance of algorithm selection in developing accurate predictive models for cardiovascular health. 2024 IEEE. Creator An entity primarily responsible for making the resource Roopini J.; Deepa B.G.; Pooja N.G. Source A related resource from which the described resource is derived 2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. 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/10.1109/NMITCON62075.2024.10698965" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NMITCON62075.2024.10698965</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207399477&amp;doi=10.1109%2FNMITCON62075.2024.10698965&amp;partnerID=40&amp;md5=363d3b55f8ea97d83123807b929d03f9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207399477&amp;doi=10.1109%2fNMITCON62075.2024.10698965&amp;partnerID=40&amp;md5=363d3b55f8ea97d83123807b929d03f9</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835037289-2 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 Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Roopini J., Reva University, School of Csa, Bangalore, India; Deepa B.G., Christ University, Dept. of Computer Science, Bangalore, India; Pooja N.G., New Horizon College, Dept. of Cse, Bangalore, India