From Conception to Deployment: Intelligent Stroke Prediction Framework using Machine Learning and Performance Evaluation

Abstract

Stroke is the second leading cause of death worldwide. Machine learning classification algorithms have been widely adopted for stroke prediction. However, these algorithms were evaluated using different datasets and evaluation metrics. Moreover, there is no comprehensive framework for stroke data analytics. This paper proposes an intelligent stroke prediction framework based on a critical examination of machine learning prediction algorithms in the literature. The five most used machine learning algorithms for stroke prediction are evaluated using a unified setup for objective comparison. Comparative analysis and numerical results reveal that the Random Forest algorithm is best suited for stroke prediction.