LearnAD: Learning Interpretable Rules for Brain Networks in Alzheimer’s Disease Classification

Thomas Andrews, Alessandra Russo, Sara Ahmadi-Abhari, Mark Law

TL;DR: Learning interpretable rules to explain AD-affected brain networks

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 extract 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.

Length: long paper (up to 8 pages)

Domain: methods

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Submission Number: 71