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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Aplikasi Isotop Radiasi WAPFI (Wahana Pendidikan Fisika) Jurnal Teknologi Informasi dan Ilmu Komputer INDONESIAN JOURNAL OF APPLIED PHYSICS Spektra: Jurnal Fisika dan Aplikasinya Jurnal ULTIMA Computing Jurnal Ilmu Fisika Indonesian Journal of Biomedicine and Clinical Sciences Indonesian Journal of Aerospace
Adhi Harmoko Saputro
Department Of Geography, Faculty Of Mathematics And Natural Sciences, University Of Indonesia, UI Depok Campus, Depok, 16424, Indonesia
Aerospace Engineering Astronomy Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Materials Science & Nanotechnology Medicine & Pharmacology Neuroscience Physics Social Sciences Other

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Hierarchical Tissue-Based MRI Features with Explainable Machine Learning for Alzheimer’s Disease Classification Muhammed B Ceesay; Adhi Harmoko Saputro; Syahril Siregar
Jurnal Ilmu Fisika Vol 18 No 1 (2026): March 2026
Publisher : Jurusan Fisika FMIPA Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jif.18.1.93-104.2026

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by multiscale structural brain degeneration. Many MRI-based machine learning approaches rely on coarse volumetric measures or black-box models with limited anatomical interpretability. This study aims to localize anatomically meaningful brain regions that discriminate AD from cognitively normal (CN) subjects using a hierarchical tissue-based (HTB) MRI framework. The method models gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) volumetric changes at lobar, gyral, and 246 fine-grained subregions defined by the Brainnetome atlas. T1-weighted MRI scans from 454 participants (227 AD, 227 CN) obtained from ADNI and MIRIAD were preprocessed using AC-PC alignment, N4 bias correction, skull stripping, and nonlinear registration to MNI space. A total of 561 HTB features were extracted to train Random Forest and XGBoost classifiers using five-fold stratified cross-validation with Bayesian hyperparameter optimization. The XGBoost model achieved the best performance (Accuracy: 79.74%, ROC-AUC: 85.07%), comparable to recent atlas-based MRI classification studies, while providing improved multiscale anatomical interpretability. SHAP analysis revealed consistent hierarchical atrophy patterns in hippocampal subregions, medial amygdala, and areas 35/36 and 28/34, demonstrating that hierarchical anatomical modeling with explainable machine learning enables transparent localization of clinically meaningful AD biomarkers without reliance on black-box architectures.
Co-Authors Arief Andy Soebroto Bayu Azmi Bimo Saputro Dimyati, Ratih Dewanti Dony Kushardono Fendi nugroho Hasan Mayditia Heru Prasetio Irene Darmawan Kustiyo Kustiyo, Kustiyo LILIK BUDIPRASETYO Miftahul Jannah Muhammed B Ceesay Nunung Nuraeni Okky Agassy Firmansyah Panggabean, Prima Ria Rumata Pramesti, Hari Agusasi Putra, Arifrahman Yustika Putri, Nurulita Purnama Ratih Prasetya, Ratih Risa Annisa Rista, Rista Rokhmatuloh, Rokhmatuloh Sampurna, Adhimukti T. Sartika Salaswati Shannaz Nadia Yusharyahya Syahril Siregar Titik Lestari Titik Lestari Wahyudi Hasbi Wibisono Wibisono Windri Handayani