STROKE PREDICTION USING MACHINE LEARNING TECHNIQUES

Abstract

Insufficient flow of blood to the brain can lead to a stroke, which kills brain cells. It is currently the major cause resulting in death worldwide. Hemorrhagic stroke and Ischemic stroke are the two prime types of stroke. Hemorrhagic stroke results from bleeding, while Ischemic stroke is caused if there is insufficient blood supply. Several risk factors that are thought to be connected to stroke have been identified through examination of affected patients. Such risk factors are used in numerous research for the prediction and classification of stroke diseases. Majority of the models are constructed using machine learning and data mining techniques. In order to know the type of stroke that may occur or has already occurred, advanced machine learning algorithms are used to analyze a person's physical state and medical report data in this study. Good number of datasets of hospital entries are made use to address the problem. The categorization result shows that the outcome is respectable and that it may be used to a real-time medical report. It is sure that these algorithms can help with better understanding of illnesses and serve as an effective healthcare partner.