1. A comparative analysis of various machine learning algorithm for heart disease prediction based on feature selection.
- Author
-
Shinde, Sagar, Kulkarni, Sukanya, and Karia, Deepak
- Subjects
- *
FEATURE selection , *HEART diseases , *MEDICAL forecasting , *DEATH rate , *COMPARATIVE studies , *MACHINE learning - Abstract
Cardiovascular disease are leading causes of mortality worldwide, accounting for a substantial fatality rate in recent years, In the last several decades, it has been the number one cause of death, not just in India but globally. It is tough for medical practitioners to forecast because it is a complex undertaking that requires expertise and advanced understanding. In the field of healthcare data analysis, predicting heart disease may be difficult. Machine learning can provide a quick and accurate decision-making solution. The main purpose of the paper is to use ML algorithms to predict presence or absence of heart disease. Various ML algorithms have been analyzed such as RF, SVM, DT, LR, XGBoost, Naive Bayes and KNN. And, out of all of these algorithms, Logistic Regression and SVM had the highest accuracy of 81.97%. The maximum accuracy, recall, precision, and f1 score is achieved by Logistic Regression and SVM, whereas the lowest accuracy, recall, precision, and f1 score is achieved by KNN. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF