Citation

Al-bakri, Fatima Hasan and Bejuri, Wan Mohd Yaakob Wan and Al-Andoli, Mohammed Nasser and Ikram, Raja Rina Raja and Khor, Hui Min and Mispan, Mohd Syafiq and Yunos, Norhazwani Md and Yusof, Noor Fazilla Abd and Fauadi, Muhammad Hafidz Fazli Md and Jaya, Abdul Syukor Mohamad and Moketar, Nor Aiza and Yusop, Noorrezam and Burhanudin, Kharismi and Marindah, Tyanita Puti and Bustamin, Anugrayani and Zainuddin, Zahir and Wahyuni, Deasy and Ariffin, Umi Kalsom (2025) A Hybrid Explainable AI Framework (HXAI) for Accurate and Interpretable Diagnosis of Alzheimer’s Disease. Diagnostics, 15 (24). p. 3118. ISSN 2075-4418

Text A Hybrid Explainable AI Framework (HXAI) for Accurate and Interpretable Diagnosis of Alzheimer’s Disease.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

Background/Objectives: In clinical practice, Explainable AI (XAI) enables non-specialists and general practitioners to make precise diagnoses. Current XAI approaches are limited, as many rely solely on either presenting explanations of clinical data or presenting explanations of MRI, or presenting explanations in unclear ways, reducing their clinical utility. Methods: In this paper, we propose a novel Hybrid Explainable AI (HXAI) framework. This framework uniquely integrates both model-agnostic (SHAP) and model-specific (Grad-CAM) explanation methods within a unified structure for the diagnosis of Alzheimer’s disease. The dual-layer explainability constitutes the main originality of this study, as it provides the possibility of interpreting quantitative (at the feature level) and spatial (at the region level) data within a single diagnostic framework. Clinical features (e.g., Mini-Mental State Examination (MMSE), normalized Whole Brain Volume (nWBV), Socioeconomic Status (SES), age) are combined with MRI-derived features extracted via ResNet50, and these features are integrated using ensemble learning with a logistic regression meta-model. Results: The corresponding validation reflects the explainability accuracy of these feature-based explanations, with removal-based tests achieving 83.61% explainability accuracy, confirming the importance of these features. Model-specific information was used to explain MRI predictions, achieving 58.16% explainability accuracy of visual explanations. Conclusions: Our HXAI framework integrates both model-agnostic and model-specific approaches in a structured manner, supported by quantitative metrics. This dual-layer interpretability enhances transparency, improves explainability accuracy, and provides an accurate and interpretable framework for AD diagnosis, bridging the gap between model accuracy and clinical trust.

Item Type:	Article
Uncontrolled Keywords:	Alzheimer’s disease
Subjects:	R Medicine > RA Public aspects of medicine > RA421-790.95 Public health. Hygiene. Preventive medicine
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	07 Jan 2026 01:25
Last Modified:	07 Jan 2026 07:01
URII:	http://shdl.mmu.edu.my/id/eprint/15151

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