Short-term MCI-to-AD prediction using MRI, neuropsychological scores and ensemble tree learning techniques

This paper analyzes the performance of ensemble tree learning techniques for predicting the progression of Alzheimer's disease (AD) in a longitudinal dataset of 128 mild-cognitive impairment (MCI) subjects from the Alzheimer's disease Neuroimage Initiative (ADNI). The system is evaluated by means of tissue-segmented T1 magnetic resonance images (MRI) as well as Mini Mental State Examination (MMSE) and cognitive assessment subscale (ADAS-Cog) scores collected every 6 months during a three-year follow-up. The ensembles are trained by means of boosting and bagging while their performance is compared in terms of the classification error and the received operating characteristics curve (ROC) using cross validation.