Stroke prediction using machine learning techniques

Stroke is a major reason for disability and mortality across the globe, making initial prediction and intervention critical t o reducing its impact. This project leverages machine learning techniques to develop a reliable and efficient system for predicting stroke risk. The model analyzes various clinical and lifestyle factors, including age, gender, hypertension, heart disease, cholesterol levels, and smoking habits, to identify individuals at high risk. The dataset used for training and evaluation is sourced from kaggle, consisting of 5110 entries. The data preprocessing phase involves managing missing values, normalization, and feature selection to enhance model accuracy. Key features are identified using techniques like correlation analysis and Recursive Feature Elimination (RFE) to improve predictive performance. The system implements advanced ML techniques implemented through Python which is embedded with many diverse libraries which caters the user requirements. To evaluate and redefine the performance of the applied ML technique, parameters including accuracy, precision, recall, and F1-score are considered. This project provides an affordable, scalable, and accurate solution for stroke prediction, equipping healthcare professionals with actionable insights for timely interventions, reducing stroke-related fatalities, and improving patient outcomes.