Brain Stroke Prediction and Analysis Using Machine Learning Techniques

The objective of this study is to apply machine learning to predict brain strokes by examining important health markers such as age, blood pressure, and medical history. The program accurately detects patients at risk of stroke using techniques including Random Forest, Decision Tree, and Logistic Regression. To ensure robustness, the prediction model is assessed using performance metrics such as precision, recall, and F1-score. The innovation also supports early intervention initiatives by offering statistical insights into key elements. This project is perfect for use in healthcare analytics and telemedicine because it is medium in complexity.