Interpretable Stroke Risk Prediction Using Machine Learning Algorithms

Abstract

Stroke is the second most common cause of death globally according to the World Health Organization (WHO). Information Technology (IT), and especially Machine Learning (ML), may be beneficial and useful in many aspects of stroke management. However, the majority of the existing studies focus on the development of ML models for confronting such cases without checking the degree of confidence and reliability of the constructed models. To strengthen models’ performance, diverse metric functions have to be estimated, also finding the most important features of the underlying datasets. Thus, this paper studies whether the results from diverse ML models are true and realistic or not, based on diverse metric functions to verify that they extract efficient and reliable results. With this in mind, a plethora of models are built to predict the likelihood of stroke, referring to Support Vector Classifier, K-Nearest Neighbors, Logistic Regression, Random Forest, XGB Classifier, and LGBM Classifier. All the captured results are compared based on the chosen metric functions, concluding into the most suitable and accurate model for stroke prediction.