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Heart Disease Prediction using SVM and RF
1 Department of Physics, Missouri University of Science and Technology, Rolla, MO, USA.
2 Faculty of Physics, University of Warsaw, Warsaw, Poland.
Research Article
International Journal of Science and Research Archive, 2024, 13(02), 1617-1623.
Article DOI: 10.30574/ijsra.2024.13.2.2410
Publication history:
Received on 08 November 2024; revised on 24 December 2024; accepted on 27 December 2024
Abstract:
Heart disease is one of the top priority health issues in the world, and early detection is paramount to enhancing the efficacy of treatment and reducing complications. Recently, machine learning has shown promises in predicting heart disease. The present study examines predictions based on random forests, and support vector machines on realistic data sets. The Boruta algorithm is used to find all the significant attributes from the dataset with respect to an outcome variable. From 13 input attributes, 6 attributes are selected including age, chest pain, maximum heart rate, ST depression induced by exercise relative to rest, number of major vessels colored by flourosopy, Thallium stress test. In this research paper, the presence of heart disease is predicted by employing Random Forest, Support Vector Machine. The F1- Score, recall and precision score of Random forest classifiers are 84.60%, 85.12% and 84.82%, respectively.
Keywords:
Heart Disease Prediction; Machine Learning; Random Forests; Support Vector Machines. Security; Privacy; IOT; Medical Internet of Things; Smart Health
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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
