According to the World Health Organization (WHO), between the years 2000 and 2019, stroke was the leading cause of death globally. Preventing strokes and providing proper care to stroke patients can save people's lives and be a cost-effective measure for healthcare-related costs. One way to achieve that is to identify their risk of stroke early on in order to take the necessary precautions to prevent it. This project aims to create an pplication that can accurately predict whether a person is at risk of stroke based on their basic health and personal metrics input. To ensure the most accurate predictions, three different classification models were implemented and then compared, and the best performing model based on accuracy, precision, recall, f-1 score, and runtime is selected. The three models to be used are complement naïve bayes, decision trees, and random forest. The results of the comparison show that naïve bayes outperforms the other methods, which will be implemented in the application where the user can check their stroke risk.
