Towards the Hybrid Approach for Predicting Stroke Risk: A Feature Augmented Model

This project addresses the critical challenge of stroke prediction by developing a hybrid model that integrates the strengths of the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Stroke risk is highly influenced by lifestyle-related factors such as smoking, hypertension, heart disease, and elevated body mass index (BMI). Although existing models, such as standalone Random Forest classifiers, offer moderate predictive performance, achieving an accuracy of approximately 74.53%, they often fall short in clinical reliability. The proposed hybrid model improves prediction accuracy by leveraging Random Forest to capture complex, nonlinear relationships and determine feature importance, while SVM enhances performance in high-dimensional spaces by establishing precise decision boundaries. This study also includes a comprehensive literature review that evaluates existing algorithms, their implementation in current systems, and cross-domain insights, ultimately forming the development of a novel conceptual framework. The anticipated outcome is a robust, data-driven predictive tool that enhances clinical decision-making and supports early intervention strategies. By combining complementary machine learning techniques, this hybrid approach aims to set a new benchmark in stroke risk assessment and contribute meaningfully to patient care in modern healthcare environments towards sustainable public health.