  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.
