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 Analysis of Cardiovascular Diseases Prediction Using Machine Learning Classification Algorithms Subject The topic of the resource Cardiovascular Diseases; COVID-19; Cross-validation; Grid Search; Healthcare Decision Making; Logistic Regression; Machine Learning; Random Forest; XGBoost Fatality Prediction Description An account of the resource Worldwide healthcare systems have faced enormous hurdles because of the COVID-19 pandemic, especially when it comes to treating individuals who already have pre-existing disorders such as cardiovascular diseases (CVDs). Prioritizing medical therapies and resources for COVID-19 patients who are at increased risk of mortality from underlying CVDs requires early identification. In this work, we investigate how well three machine learning algorithms-, Random Forest, XGBoost, and Logistic Regression-predict death in COVID-19 patients who already have cardiovascular disease. We performed grid search and cross-validation using a dataset of clinical and demographic features of COVID-19 patients with and without CVDs to reduce overfitting and maximize model performance. Our findings show that among patients with CVDs, Logistic Regression had the best accuracy in predicting COVID-19 fatality, followed by Random Forest and Decision Tree coming in a close second. These results highlight how machine learning algorithms can help clinical professionals detect high-risk COVID-19 patients who have underlying cardiovascular diseases (CVDs), enable prompt interventions, and enhance patient outcomes. 2024 IEEE. Creator An entity primarily responsible for making the resource Srivastava S.; Upreti K.; Shanbhog M. Source A related resource from which the described resource is derived Proceedings - 3rd International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 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/ACCAI61061.2024.10601806" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ACCAI61061.2024.10601806</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200571096&amp;doi=10.1109%2FACCAI61061.2024.10601806&amp;partnerID=40&amp;md5=1ac9f11c8604d1f393c01f3597ac45a1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200571096&amp;doi=10.1109%2fACCAI61061.2024.10601806&amp;partnerID=40&amp;md5=1ac9f11c8604d1f393c01f3597ac45a1</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038943-2 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 Srivastava S., Christ (Deemed to Be University), Department of Computer Science, NCR, Delhi, India; Upreti K., Christ (Deemed to Be University), Department of Computer Science, NCR, Delhi, India; Shanbhog M., Christ (Deemed to Be University), Department of Computer Science, NCR, Delhi, India