Machine Learning Workflow to Explain Black-box Models for Early Alzheimer's Disease Classification Evaluated for Multiple Datasets. (arXiv:2205.05907v2 [cs.LG] UPDATED)

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 …

alzheimer's arxiv classification datasets disease machine machine learning workflow