LearnAD: Learning Interpretable Rules for Brain Networks in Alzheimers Disease Classification

📝 Original Info

• Title: LearnAD: Learning Interpretable Rules for Brain Networks in Alzheimers Disease Classification
• ArXiv ID: 2601.00877
• Date: 2025-12-30
• Authors: Thomas Andrews, Mark Law, Sara Ahmadi-Abhari, Alessandra Russo

📝 Abstract

We introduce LearnAD, a neuro-symbolic method for predicting Alzheimer's disease from brain magnetic resonance imaging data, learning fully interpretable rules. LearnAD applies statistical models, Decision Trees, Random Forests, or GNNs to identify relevant brain connections, and then employs FastLAS to learn global rules. Our best instance outperforms Decision Trees, matches Support Vector Machine accuracy, and performs only slightly below Random Forests and GNNs trained on all features, all while remaining fully interpretable. Ablation studies show that our neuro-symbolic approach improves interpretability with comparable performance to pure statistical models. LearnAD demonstrates how symbolic learning can deepen our understanding of GNN behaviour in clinical neuroscience. Recently, with the availability of large datasets such as ADNI, deep convolutional neural networks (CNNs) have been shown to learn discriminative patterns from minimally preprocessed MRI data [11] .

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