Machine learning algorithms for stroke risk prediction leveraging on explainable artificial intelligence techniques (XAI).

Ugbomeh, Ogochukwu; Yiye, Versse; Ibeke, Ebuka; Ezenkwu, Chinedu Pascal; Sharma, Vandana; Alkhayyat, Ahmed

Abstract

Stroke poses a significant global health challenge, contributing to widespread mortality and disability. Identifying predictors of stroke risk is crucial for enabling timely interventions, thereby reducing the increasing impact of strokes. This research addresses this imperative by employing Explainable Artificial Intelligence (XAI) techniques to pinpoint stroke risk predictors. To bridge existing gaps, six machine learning models were assessed using key performance metrics. Utilising the Synthetic Minority Over-sampling Technique (SMOTE) to minimize the impact of the imbalanced nature of the dataset used in this research, the Random Forest algorithm emerged as the most effective among the algorithms with an accuracy of 94.5%, AUC-ROC of 0.95, recall of 0.96, precision of 0.93, and an F1 score of 0.95. This study explored the interpretation of these algorithms and results using Local Interpretable Model-agnostic Explanations (LIME) and Explain Like I'm Five (ELI5). With the interpretations, healthcare providers can gain insight into patients' stroke risk predictions. Key stroke risk factors highlighted by the study include Age, Marital Status, Glucose Level, Body Mass Index, Work Type, Heart Disease, and Gender. This research significantly contributes to healthcare and healthcare informatics by providing insights that can enhance strategies for stroke prevention and management, ultimately leading to improved patient care. The identified predictors offer valuable information for healthcare professionals to develop targeted interventions, fostering a proactive approach to mitigating the impact of strokes on individuals and the healthcare system.

Citation

UGBOMEH, O., YIYE, V., IBEKE, E., EZENKWU, C.P., SHARMA, V. and ALKHAYYAT, A. 2024. Machine learning algorithms for stroke risk prediction leveraging on explainable artificial intelligence techniques (XAI). In Proceedings of the 2024 International conference on electrical, electronics and computing technologies (ICEECT 2024), 29-31 August 2024, Greater Noida, India. Piscataway: IEEE [online], article 10739320. Available from: https://doi.org/10.1109/ICEECT61758.2024.10739320

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 International conference on electrical, electronics and computing technologies (ICEECT 2024)
• Start Date: Aug 29, 2024
• End Date: Aug 31, 2024
• Acceptance Date: Jul 11, 2024
• Online Publication Date: Aug 29, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Jul 16, 2024
• Publicly Available Date: Aug 29, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Article Number: 10739320
• DOI: https://doi.org/10.1109/ICEECT61758.2024.10739320
• Keywords: Health risk factors; Health technologies; Artificial intelligence (AI); Explainable artificial intelligence (XAI)
• Public URL: https://rgu-repository.worktribe.com/output/2413161