Analysis of Machine Learning Models for Stroke Prediction with Emphasis on Hyperparameter Tuning Techniques

Stroke remains a significant global cause of death and disability, necessitating early and accurate prediction models for prompt intervention. This study contrasts the performance of Support Vector Machine (SVM) and Random Forest (RF) models to enhance stroke prediction approaches. Emphasizing the critical role of hyper parameter adjustment in improving model efficiency, two tuning methods—Grid Search Cross-Validation (GS-CV) and Randomized Search Cross-Validation (RS-CV)—are investigated. Data prepossessing utilizes a data set from the Medical Clinic of Bangladesh, comprising 5,110 patient records. Imbalanced data is addressed through the Synthetic Minority Over-sampling Technique (SMOTE). Despite being good at predicting accuracy, SVM with RS-CV tuning is more accurate, achieving a 96% accuracy than RF with GS-CV tuning that achieves 92% accuracy. Such outcomes highlight the significance of choosing proper hyperparameter tuning techniques and ML models for stroke prediction. They also imply an outlet for use in healthcare contexts concerning early identification and prophylactic steps. This comparison study adds to the current debate about machine learning in medical prediction, focusing on the methodological aspects critical to constructing reliable and effective predictive systems.