Predicting the pace of MCI progression to AD based on multi‐modality neuroimaging data by a novel hybrid ordinal learning algorithm

Machine learning (ML) has shown great promise for integrating multi‐modality neuroimaging datasets to predict the risk of converting to AD for individuals with Mild Cognitive Impairment (MCI). Most existing work aims to classify MCI patients into converters versus non‐converters. The limitation is a lack of granularity in differentiating MCI patients who convert at different paces. Pace prediction has important clinical values by allowing for more personalized interventional strategies, better preparing patients and their caregivers, and facilitating patient selection in clinical trials. We proposed a novel Hybrid Ordinal Learning (HOL) algorithm for pace prediction of MCI conversion to AD.