Abstract
Nowadays, cardiovascular diseases are considered as one of the fatal and main reasons for mortality all around the globe. The mortality or high-risk rate can be reduced if an early detection system for cardiovascular disease is introduced. A massive amount of data gets collected by healthcare organizations. A proper and careful study regarding the data can be carried out to extract some important and interesting insight that may help out the professionals. Keeping that in mind, in this paper, at first six distinct machine learning algorithms(Logistic Regression, SVM, KNN, Naïve Bayes, Random Forest, Gradient Boosting) were applied to four different datasets encompasses different set of features to show their performance over them. Secondly, the prediction accuracy of the ML algorithms was analyzed to find out the best set of features and the best algorithm to predict cardiovascular diseases. The results find out the best suited eleven feature and also showed that Random Forest performs well in terms of accuracy in predicting cardiovascular diseases.
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Jalal, M.M., Tasnim, Z., Islam, M.N. (2021). Exploring the Machine Learning Algorithms to Find the Best Features for Predicting the Risk of Cardiovascular Diseases. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_49
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