Heart Stroke Prediction Using Machine Learning Algorithms

Abstract

A Stroke is a disease when there is insufficient blood supply to the brain, which causes cell death. It is currently the world’s biggest cause of death. Upon examining the affected individuals, a number of risk variables that are thought to be connected to the cause of stroke have been identified. Numerous studies have been conducted to predict and categorize stroke disorders using the risk variables. The majority of the models are built using machine learning and data mining technologies. In this work, we have employed four machine learning algorithms to identify the type of stroke that may have happened based on medical report data and an individual’s physical condition. We have gathered a sizable amount of hospital entries. This study employs many methodologies, including decision trees, Naive Bayes, ANN algorithm, and Random Forest algorithm. Thus, the aim of this study is to evaluate the mentioned algorithms and determine which one does the task more accurately. After completing all of the evaluations, we can conclude that the Random Forest method has the highest accuracy of all the algorithms with 99%.