Assessing the Interpretability of Machine Learning Models in Early Detection of Alzheimer's Disease

Alzheimer's disease (AD) is a chronic and irreversible neurological disorder, making early detection essential for managing its progression.This study investigates the coherence of SHAP values with medical scientific truth.It examines three types of features: clinical, demographic, and FreeSurfer extracted from MRI scans.A set of six ML classifiers are investigated for their interpretability levels.This study is validated on the OASIS-3 dataset with binary classification.The results show that clinical data outperforms the others, with a margin of 14% over FreeSurfer features, the second-best features.In the case of clinical features, the explanations provided by the tree-based classifiers consistently align with medical insights.This comparison was calculated using the Kendall Tau distance.