Comparative Analysis of Stroke Prediction Models Using Machine Learning

This study underscores the potential of machine learning in stroke risk prediction, addressing key challenges such as class imbalance and feature selection. Our findings highlight the significant role of demographic, clinical, and lifestyle factors, with age, average glucose level, and BMI emerging as key predictors. Notably, the analysis reveals the importance of age-specific models, as the predictive influence of factors shifts across different age groups. In elderly patients (65-80 years), work type and glucose level become more influential, while hypertension and heart disease gain prominence. By achieving high predictive accuracy and identifying im-pactful features, this research supports the development of stroke risk assessment tools with potential integration into clinical decision systems. These tools could assist clinicians in early intervention planning and personalized prevention strategies. However, challenges such as data variability, model interpretability, and deployment in real-world healthcare settings remain. Future research should focus on improving model sensitivity, incorporating diverse datasets, and validating predictions in clinical environments. Advancing these models could enhance early detection strategies, ultimately improving patient outcomes and stroke prevention.