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Heart Disease Prediction using SVM
1 Department of Computer Science, North Dakota State University, Fargo, North Dakota, ND 58105, USA.
2 Department of Electrical and Computer Engineering, Lamar University, Beaumont, Texas, TX 77710, USA.
3 Department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh.
Research Article
International Journal of Science and Research Archive, 2024, 11(02), 412–420.
Article DOI: 10.30574/ijsra.2024.11.2.0435
Publication history:
Received on 03 February 2024; revised on 11 March 2024; accepted on 14 March 2024
Abstract:
Diagnosing and predicting the outcome of cardiovascular disease are essential tasks in medicine that help ensure patients receive accurate classification and treatment from cardiologists. The use of machine learning in the healthcare sector has grown due to its ability to identify patterns in data. By applying machine learning techniques to classify the presence of cardiovascular diseases, it's possible to decrease the rate of misdiagnosis. This study aims to create a model capable of accurately forecasting cardiovascular diseases to minimize the deaths associated with these conditions. In this paper, two types of SVM model such as linear SVM and polynomial SVM is used. Accuracy, precision, recall and F1 score has been evaluated for comparing linear SVM and polynomial SVM. Polynomial SVM provides better accuracy than linear SVM.
Keywords:
Heart disease; Machine Learning; Precision; Recall; SVM; Accuracy.
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
