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 Faculty Publications 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/. Creator An entity primarily responsible for making the resource Sushmitha, M.T.; Saxena, Surabhi; Singhal, Neha Title A name given to the resource Evaluate Machine Learning Techniques for Early Disease of Cardiovascular Disease Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 5th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICMNWC66779.2025.11354433" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICMNWC66779.2025.11354433</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033716958?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033716958?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sushmitha M.T., Christ University, Department of Computer Science, Bangalore, India; Saxena S., Christ University, Department of Computer Science, Bangalore, India; Singhal N., Christ University, Department of Computer Science, Bangalore, India Description An account of the resource Cardiovascular diseases are one of the major causes of death around the world, and their early detection is critical for effective intervention. The paper presents a systematic review of machine learning techniques used for the early prediction of cardiovascular diseases, focusing on studies carried out between 2019 and 2024. Widely used models considered in the review include Logistic Regression, Support Vector Machines, Random Forest, K-Nearest Neighbors, Gradient Boosting, and hybrid ensemble methods with the aim of ascertaining predictive accuracy, interpretability, and clinical relevance. In most of the reviewed studies, ensemble and Random Forest models attained the highest accuracies of 90% - 98%, while Gradient Boosting and SVMs were mostly above 90% in balanced datasets. Logistic Regression had a moderate accuracy of 85%-91% but remained the most interpretable, while KNN established the lowest performance of 80%-86%. Despite the promising strides, there are a number of limitations, such as imbalance in datasets, limited external validation, and small benchmark datasets, that are limiting general application in health. This systematic review highlights strengths and weaknesses of the contemporary machine learning approaches and makes it evident that clinically validated, interpretable, and generalizable models should be developed in order to assist real-world medical decision-making. 2025 IEEE. Subject The topic of the resource Early; Heart Disease Prediction; Machine Learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155664-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online