Explainable AI for Stroke Prediction: A Comprehensive Approach to Enhancing Transparency and Interpretability

Stroke is among the leading causes of death, especially among old adults. Thus, the mortality rate and severe cerebral disability can be avoided when stroke is diagnosed at its early stages, followed by subsequent treatment. There is no doubt that healthcare specialists can find the necessary solutions more effectively and instantly with the help of artificial intelligence (AI) and machine learning (ML). In this study, we used ML classifiers and explainable artificial intelligence (XAI) to predict stroke. Six different ML classifiers that trained on available datasets for stroke patients. Six feature selection methodologies were used to extract essential features from the dataset. The XAI methods applied (Shapley Additive Values (SHAP), ELI5, and Local Interpretable Model-agnostic Explanations (LIME)). This study will help medical practitioners to effectively manage their patients and develop a screening tool that incorporates technologies to revolutionize the approach to preventing and treating stroke.