A machine learning framework with uncertainty‐driven neuroimaging modality selection for data‐efficient prediction of Alzheimer’s Disease

Early prediction of the AD risk for individuals with MCI has important clinical value. Machine Learning (ML) models integrating multi‐modality neuroimaging datasets have shown great promise. Existing approaches either require all the modalities or suffer from performance loss with fewer modalities. We proposed a data‐efficient ML framework, namely the Uncertainty‐driven Modality Selection (UMoS) framework, to sequentially add modalities for each patient on an as‐needed basis while at the same time ensuring high prediction accuracy.