Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer's disease classification
This work addresses Alzheimer's disease diagnosis using MRI data, but it is incremental as it extends an existing method to a hierarchical setting.
The authors tackled Alzheimer's disease classification by extending Stacked Penalized Logistic Regression (StaPLR) to handle hierarchical multi-view MRI data, resulting in improved classification performance over elastic net regression.
Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.