Predicting Stroke Risk Using Machine Learning: A Data-Driven Approach to Early Detection and Prevention

Abstract

Stroke is a major global health concern and a leading cause of disability and mortality, emphasizing the need for early risk prediction and intervention. This study leverages statistical analysis, machine learning (ML) classification, clustering, and survival modeling to identify key stroke predictors using a dataset of 5110 records. Descriptive statistics reveal that age, glucose levels, BMI, hypertension, and heart disease are the most influential risk factors. Stroke prevalence is notably higher among hypertensive (13.25%) and heart disease patients (17.03%), as well as among former (7.91%) and current smokers (5.32%). Clustering analysis using PCA and t-SNE highlights high-risk groups with elevated glucose levels and advanced age. Among ML models, XGBoost offers the best trade-off between precision and recall, while na & iuml;ve Bayes achieves the highest recall (0.404), detecting more stroke cases despite higher false positives. Feature importance analysis ranks glucose, BMI, and age as dominant predictors, with XGBoost emphasizing cardiovascular conditions. Survival analysis confirms increasing stroke risk beyond age 60, with the Kaplan-Meier and Cox models showing a 31.9% risk increase linked to hypertension. These findings underscore the importance of early screening, lifestyle intervention, and targeted care. Future research should explore data-balancing methods like SMOTE and develop real-time tools to support clinical decision-making.