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A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis
 
 
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Article

A Feature-Augmented Explainable Artificial Intelligence Model for Diagnosing Alzheimer’s Disease from Multimodal Clinical and Neuroimaging Data

by
Fatima Hasan Al-bakri
1,
Wan Mohd Yaakob Wan Bejuri
1,2,*,
Mohamed Nasser Al-Andoli
3,*,
Raja Rina Raja Ikram
1,
Hui Min Khor
4,
Yus Sholva
5,
Umi Kalsom Ariffin
6,
Noorayisahbe Mohd Yaacob
7,
Zuraida Abal Abas
2,
Zaheera Zainal Abidin
2,
Siti Azirah Asmai
2,
Asmala Ahmad
1,
Ahmad Fadzli Nizam Abdul Rahman
1,
Hidayah Rahmalan
1 and
Md Fahmi Abd Samad
8
1
Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka 76100, Malaysia
2
Faculty of Artificial Intelligence and Cyber Security, Universiti Teknikal Malaysia Melaka, Melaka 76100, Malaysia
3
Faculty of Computing Informatics, Multimedia University, Cyberjaya 63100, Malaysia
4
Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia
5
Fakultas Teknik, Universitas Tanjungpura, Pontianak 78124, Indonesia
6
Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43200, Malaysia
7
Centre for Software Technology and Management, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
8
Faculty of Mechanical Technology and Engineering, Universiti Teknikal Malaysia Melaka, Melaka 75450, Malaysia
*
Authors to whom correspondence should be addressed.
Diagnostics 2025, 15(16), 2060; https://doi.org/10.3390/diagnostics15162060
Submission received: 26 July 2025 / Revised: 12 August 2025 / Accepted: 16 August 2025 / Published: 17 August 2025
(This article belongs to the Special Issue Explainable Machine Learning in Clinical Diagnostics)

Abstract

Background/Objectives: This study presents a survey-based evaluation of an explainable AI(Feature-Augmented) approach, which was designed to support the diagnosis of Alzheimer’s disease (AD) by integrating clinical data, MMSE scores, and MRI scans. The approach combines rule-based reasoning and example-based visualization to improve the explainability of AI-generated decisions. Methods: Five doctors participated in the survey: two with 6 to 10 years of experience and three with more than 10 years of experience in the medical field and expertise in AD. The participants evaluated different AI outputs, including clinical feature-based interpretations, MRI-based visual heat maps, and a combined interpretation approach. Results: The model achieved a 100% trust score, with 20% of the participants reporting full trust and 80% expressing conditional trust, understanding the diagnosis but seeking further clarification. Overall, the participants reported that the integrated explanation format improved their understanding of the model decisions and enhanced their confidence in using AI-assisted diagnosis. Conclusions: To our knowledge, this paper is the first to gather the views of medical experts to evaluate the explainability of an AI decision-making model when diagnosing AD. These preliminary findings suggest that explainability plays a key role in building trust and ease of use of AI tools in clinical settings, especially when used by experienced clinicians to support complex diagnoses, such as AD.
Keywords: Alzheimer’s disease; artificial intelligence; explainable AI; ensemble learning; meta-model; clinical data; MRI; MMSE; lateral ventricles; CNN; clinical decision; SHAP; Grad-Cam Alzheimer’s disease; artificial intelligence; explainable AI; ensemble learning; meta-model; clinical data; MRI; MMSE; lateral ventricles; CNN; clinical decision; SHAP; Grad-Cam

Share and Cite

MDPI and ACS Style

Al-bakri, F.H.; Bejuri, W.M.Y.W.; Al-Andoli, M.N.; Ikram, R.R.R.; Khor, H.M.; Sholva, Y.; Ariffin, U.K.; Yaacob, N.M.; Abas, Z.A.; Abidin, Z.Z.; et al. A Feature-Augmented Explainable Artificial Intelligence Model for Diagnosing Alzheimer’s Disease from Multimodal Clinical and Neuroimaging Data. Diagnostics 2025, 15, 2060. https://doi.org/10.3390/diagnostics15162060

AMA Style

Al-bakri FH, Bejuri WMYW, Al-Andoli MN, Ikram RRR, Khor HM, Sholva Y, Ariffin UK, Yaacob NM, Abas ZA, Abidin ZZ, et al. A Feature-Augmented Explainable Artificial Intelligence Model for Diagnosing Alzheimer’s Disease from Multimodal Clinical and Neuroimaging Data. Diagnostics. 2025; 15(16):2060. https://doi.org/10.3390/diagnostics15162060

Chicago/Turabian Style

Al-bakri, Fatima Hasan, Wan Mohd Yaakob Wan Bejuri, Mohamed Nasser Al-Andoli, Raja Rina Raja Ikram, Hui Min Khor, Yus Sholva, Umi Kalsom Ariffin, Noorayisahbe Mohd Yaacob, Zuraida Abal Abas, Zaheera Zainal Abidin, and et al. 2025. "A Feature-Augmented Explainable Artificial Intelligence Model for Diagnosing Alzheimer’s Disease from Multimodal Clinical and Neuroimaging Data" Diagnostics 15, no. 16: 2060. https://doi.org/10.3390/diagnostics15162060

APA Style

Al-bakri, F. H., Bejuri, W. M. Y. W., Al-Andoli, M. N., Ikram, R. R. R., Khor, H. M., Sholva, Y., Ariffin, U. K., Yaacob, N. M., Abas, Z. A., Abidin, Z. Z., Asmai, S. A., Ahmad, A., Abdul Rahman, A. F. N., Rahmalan, H., & Abd Samad, M. F. (2025). A Feature-Augmented Explainable Artificial Intelligence Model for Diagnosing Alzheimer’s Disease from Multimodal Clinical and Neuroimaging Data. Diagnostics, 15(16), 2060. https://doi.org/10.3390/diagnostics15162060

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