Citation

Al-bakri, Fatima Hasan and Wan Bejuri, Wan Mohd Yaakob and Al-Andoli, Mohamed Nasser and Raja Ikram, Raja Rina and Khor, Hui Min and Tahir, Zulkifli (2025) A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis. Diagnostics, 15 (13). p. 1642. ISSN 2075-4418

Text A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Background/Objectives: Artificial intelligence (AI) models for Alzheimer’s disease (AD) diagnosis often face the challenge of limited explainability, hindering their clinical adoption. Previous studies have relied on full-scale MRI, which increases unnecessary features, creating a “black-box” problem in current XAI models. Methods: This study proposes an explainable ensemble-based diagnostic framework trained on both clinical data and midslice axial MRI from the ADNI and OASIS datasets. The methodology involves training an ensemble model that integrates Random Forest, Support Vector Machine, XGBoost, and Gradient Boosting classifiers, with meta-logistic regression used for the final decision. The core contribution lies in the exclusive use of mid-slice MRI images, which highlight the lateral ventricles, thus improving the transparency and clinical relevance of the decisionmaking process. Our mid-slice approach minimizes unnecessary features and enhances model explainability by design. Results: We achieved state-of-the-art diagnostic accuracy: 99% on OASIS and 97.61% on ADNI using clinical data alone; 99.38% on OASIS and 98.62% on ADNI using only mid-slice MRI; and 99% accuracy when combining both modalities. The findings demonstrated significant progress in diagnostic transparency, as the algorithm consistently linked predictions to observed structural changes in the dilated lateral ventricles of the brain, which serve as a clinically reliable biomarker for AD and can be easily verified by medical professionals. Conclusions: This research presents a step toward more transparent AI-driven diagnostics, bridging the gap between accuracy and explainability in XAI.

Item Type:	Article
Uncontrolled Keywords:	Alzheimer’, clinical data
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics R Medicine > RC Internal medicine > RC71-78.7 Examination. Diagnosis
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	29 Jul 2025 06:34
Last Modified:	29 Jul 2025 06:34
URII:	http://shdl.mmu.edu.my/id/eprint/14402

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