Heart Stroke Prediction

Heart stroke is one of the leading causes of mortality worldwide, and early prediction can significantly reduce the risk of fatal outcomes. This project aims to develop an efficient and accurate machine learning model to predict the likelihood of a heart stroke based on various medical and lifestyle parameters. By analyzing patient data such as age, gender, hypertension, heart disease, smoking status, body mass index (BMI), average glucose level, and work type, the model learns patterns associated with increased stroke risk. Several supervised learning algorithms, including Logistic Regression, Random Forest, Support Vector Machines (SVM), and Gradient Boosting, are implemented and compared to identify the most effective approach. Data preprocessing techniques such as handling missing values, feature scaling, and class balancing using SMOTE are applied to improve model performance. Evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC are used to assess the model's predictive power. The results demonstrate that machine learning can serve as a powerful tool in the early detection of heart stroke risk, potentially assisting healthcare professionals in making informed clinical decisions. This system can be further integrated into healthcare applications to provide real-time, data-driven support for stroke prevention strategies.