A comparative study of machine learning approaches for heart stroke prediction

Abstract

The majority of strokes are triggered by the heart and brain blocking expected pathways. Today, it is the most common cause of death in the worldwide. By looking at the people affected, several risk elements that are thought to be connected to the stroke's cause have been determined. Numerous studies have been conducted for the prediction and categorization of stroke diseases using these risk variables. Similar to any diseases, an early diagnosis of a stroke can avert such occurrences and open the door to a healthy life. Machine learning (ML) techniques have been used in this study to accurately determine heart attacks. In order to determine multiple matrices like accuracy, recall, ROC, precision, and F1 score, we used nine different machine learning algorithms in this study, which include support vector machines (SVM), K-nearest neighbor (KNN), XGBoost, AdaBoost, Random Forest (RF), Decision Tree, LightGBM, and Logistic Regression. The results indicate that the Random Forest method outperformed the others with an accuracy of 98.4%.