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.