Performance of Machine Learning Algorithms for Heart Disease Prediction: Logistic Regressions Regularized by Elastic Net, SVM, Random Forests, and Neural Networks

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dc.contributor.authorIkpea, Obehi Winnifred
dc.contributor.authorHan, David
dc.date.accessioned2023-02-17T17:11:59Z
dc.date.available2023-02-17T17:11:59Z
dc.date.issued2022-12
dc.description.abstractHeart disease, a medical condition caused by plaque buildup in the walls of the arteries, is the leading cause of death in the U.S. and worldwide. About 697,000 people suffer from this condition in the U.S. alone. This research project aims to assess and compare the performance of several classification algorithms for predicting heart disease so that the method can be considered as a clinical indicator of cardiovascular health. These methods include multiple logistic regression regularized with or without elastic nets, support vector machine, random forest, and artificial neural networks. A low prevalence of the disease is reflected in the data imbalance, and an oversampling technique is also suggested to deal with the computational challenges posed by this data imbalance.en_US
dc.description.departmentManagement Science and Statisticsen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12588/1753
dc.language.isoen_USen_US
dc.publisherUTSA Office of Undergraduate Researchen_US
dc.relation.ispartofseriesThe UTSA Journal of Undergraduate Research and Scholarly Work;Volume 8
dc.subjectundergraduate student worksen_US
dc.subjectartificial neural networksen_US
dc.subjectelastic neten_US
dc.subjectheart disease predictionen_US
dc.subjectlogistic regressionen_US
dc.subjectmachine learning algorithmsen_US
dc.subjectrandom forestsen_US
dc.subjectsupport vector machineen_US
dc.titlePerformance of Machine Learning Algorithms for Heart Disease Prediction: Logistic Regressions Regularized by Elastic Net, SVM, Random Forests, and Neural Networksen_US
dc.typeArticleen_US

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