Machine Learning Workflow to Explain Black-box Models for Early Alzheimer's Disease Classification Evaluated for Multiple Datasets
Purpose: Hard-to-interpret Black-box Machine Learning (ML) were often used
for early Alzheimer's Disease (AD) detection.
  Methods: To interpret eXtreme Gradient Boosting (XGBoost), Random Forest
(RF), and Support Vector Machine (SVM) black-box models a workflow based on
Shapley values was developed. All models were trained on the Alzheimer's
Disease Neuroimaging Initiative (ADNI) dataset and evaluated for an independent
ADNI test set, as well as the external Australian Imaging and Lifestyle
flagship study of Ageing (AIBL), and Open Access Series of Imaging Studies
(OASIS) datasets. Shapley values were compared to intuitively interpretable
Decision Trees (DTs), and Logistic Regression (LR), as well as natural and
permutation feature importances. To avoid the reduction of the explanation
validity caused by correlated features, forward selection and aspect
consolidation were implemented.
  Results: Some black-box models outperformed DTs and LR. The forward-selected
features correspond to brain areas previously associated with AD. Shapley
values identified biologically plausible associations with moderate to strong
correlations with feature importances. The most important RF features to
predict AD conversion were the volume of the amygdalae, and a cognitive test
score. Good cognitive test performances and large brain volumes decreased the
AD risk. The models trained using cognitive test scores significantly
outperformed brain volumetric models ($p<0.05$). Cognitive Normal (CN) vs. AD
models were successfully transferred to external datasets.
  Conclusion: In comparison to previous work, improved performances for ADNI
and AIBL were achieved for CN vs. Mild Cognitive Impairment (MCI)
classification using brain volumes. The Shapley values and the feature
importances showed moderate to strong correlations.