An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease

TL;DR

This paper introduces an interpretable deep learning framework that combines explainable boosting machines with imaging biomarkers for early Alzheimer's diagnosis, achieving high accuracy and interpretability.

Contribution

It proposes a novel framework integrating EBM with deep learning-based features, enhancing interpretability and performance in Alzheimer's disease classification.

Findings

Achieved 0.883 accuracy and 0.970 AUC on ADNI dataset.

Validated on external data with 0.778 accuracy and 0.887 AUC.

Outperformed traditional EBM and CNN models.

Abstract

Machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer's Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions. Explainable Boosting Machines (EBMs) are interpretable machine learning models based on the statistical framework of generalized additive modeling, but have so far only been used for tabular data. Therefore, we propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. The proposed framework is interpretable because it provides the importance of each feature. We validated the proposed framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and area-under-the-curve (AUC) of 0.970 on AD and control classification. Furthermore, we validated the proposed framework on an external testing set, achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive decline (SCD) classification. The proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features, as well as an end-to-end convolutional neural network (CNN) with optimized architecture.