Optimizing Stroke Risk Prediction: A Primary Dataset‐Driven Ensemble Classifier With Explainable Artificial Intelligence
Our study demonstrates that the novel ensemble classifier, supported by effective preprocessing and XAI techniques, is a powerful tool for stroke prediction. The high accuracy rates achieved validate its effectiveness and potential for practical clinical application. Future work will focus on incorporating deep learning techniques and medical imaging to further improve classification accuracy and model performance.
Authors
- Md. Shohidul Islam (ORCID: https://orcid.org/0000-0002-0786-5221)
- Rakib Hasan (ORCID: https://orcid.org/0000-0002-8496-3829)
- Md Khairul Islam (ORCID: https://orcid.org/0000-0002-6973-1536)
- Md. Maruf Hossain
- Md. Rakibul Islam (ORCID: https://orcid.org/0000-0002-2547-1090)
- Md Shahariar Alam
- Md. Rakibul Hasan Rakib
- Mohammad Osama Zia
- Md. Mahfuz Ahmed
Institutions
- Islamic University (BD)
- Northern University Bangladesh (BD)
Publication Details
- Journal
- Health Science Reports
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1002/hsr2.70799
- Citations
- 4
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 5.42