Machine Learning Algorithms for Stroke Risk Prediction Leveraging on Explainable Artificial Intelligence Techniques (XAI)
- Title
- Machine Learning Algorithms for Stroke Risk Prediction Leveraging on Explainable Artificial Intelligence Techniques (XAI)
- Creator
- Ugbomeh O.; Yiye V.; Ibeke E.; Ezenkwu C.P.; Sharma V.; Alkhayyat A.
- Description
- 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. 2024 IEEE.
- Source
- 2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Explain Like I'm Five (ELI5); Explainable Artificial Intelligence (XAI); Local Interpretable Model Agnostic Explanation (LIME); Machine Learning
- Coverage
- Ugbomeh O., Robert Gordon University, School of Creative and Cultural Business, Aberdeen, United Kingdom; Yiye V., Robert Gordon University, School of Creative and Cultural Business, Aberdeen, United Kingdom; Ibeke E., Robert Gordon University, School of Computing, Engrg.,and Tech, Aberdeen, United Kingdom; Ezenkwu C.P., Robert Gordon University, School of Computing, Engrg.,and Tech, Aberdeen, United Kingdom; Sharma V., Christ University, Computer Science Department, India; Alkhayyat A., Islamic University, College of Technical Engineering, Najaf, Iraq
- Rights
- Restricted Access
- Relation
- ISBN: 979-835037809-2
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Ugbomeh O.; Yiye V.; Ibeke E.; Ezenkwu C.P.; Sharma V.; Alkhayyat A., “Machine Learning Algorithms for Stroke Risk Prediction Leveraging on Explainable Artificial Intelligence Techniques (XAI),” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19041.