BRAIN STROKE PREDICTION USING MACHINE LEARNING

Brain stroke is a serious medical illness that may result in severe neurological damage or even death if left undetected and untreated. Predicting the risk of stroke at an early stage can substantially improve clinical decision-making and prevention care. The proposed work involves using machine learning for predicting the risk of brain stroke based on patient health data. The used dataset includes related features like age, Hypertension, heart disease, smoking status, BMI, glucose level, and work type was employed. Different machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and K- Nearest Neighbors (KNN) were tried and evaluated against performance measures such as accuracy, precision, recall, and F1-score. The highest prediction accuracy was found in the random Forest model, implying that it could be used to accurately classify based on this problem. This research demonstrates the potential of machine learning to enhance stroke diagnosis and assist Healthcare decision – making.