Brain Stroke Prediction Using Machine Learning

Stroke is a major global health concern and remains one of the leading causes of death and long term disability. Early prediction of stroke risk plays an important role in preventive healthcare and can significantly reduce mortality rates. With the increasing availability of healthcare data, machine learning techniques provide powerful tools for analyzing patient information and identifying risk patterns. This research presents a machine learning based approach for predicting the probability of brain stroke using patient health parameters such as age, hypertension, heart disease, body mass index, glucose level, and lifestyle factors. Various machine learning algorithms are explored and evaluated to determine their effectiveness in predicting stroke risk. The proposed system demonstrates how data driven models can support healthcare professionals in early diagnosis and preventive care.