Automated Stroke Prediction Using Machine Learning: An Explainable and Exploratory Study With a Web Application for Early Intervention

作     者：Mridha, Krishna Ghimire, Sandesh Shin, Jungpil Aran, Anmol Uddin, Md. Mezbah Mridha, M. F. 

作者机构：Marwadi Univ Dept Comp Engn Rajkot 360003 India Univ Aizu Dept Comp Sci & Engn Aizu Wakamatsu 9658580 Japan Amer Int Univ Bangladesh Dept Comp Sci Dhaka 1229 Bangladesh 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2023年第11卷

页      面：52288-52308页

核心收录：

基　　金：Competitive Research Fund of The University of Aizu  Japan 

主　　题：Stroke (medical condition) Machine learning Predictive models Prediction algorithms Machine learning algorithms Medical services Medical diagnostic imaging Stroke prediction data leakage explainable machine learning ANOVA test SHAPE LIME 

摘      要：Stroke is a dangerous medical disorder that occurs when blood flow to the brain is disrupted, resulting in neurological impairment. It is a big worldwide threat with serious health and economic implications. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention and perhaps save lives. The number of people at risk for stroke is growing as the population ages, making precise and effective prediction systems increasingly critical. wo In a comparison examination with six well-known classifiers, the effectiveness of the proposed ML technique was explored in terms of metrics relating to both generalization capability and prediction accuracy. To give insight into the black-box machine learning models, we also studied two kinds of explainable techniques, namely SHAP and LIME, in this study. SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) are well-established and reliable approaches for explaining model decision-making, particularly in the medical industry. The findings of the experiment revealed that more complicated models outperformed simpler ones, with the top model obtaining almost 91% accuracy and the other models achieving 83-91% accuracy. The proposed framework, which includes global and local explainable methodologies, can aid in standardizing complicated models and gaining insight into their decision-making, which can enhance stroke care and treatment.