Automated MRI prediction of Alzheimer's disease development by machine learning methods

Abstract

Alzheimer's is an irreversible brain disease that impairs memory, thinking and behavior and leads ultimately to death.Research has shown that individuals with MCI (mild cognitive impairment), the pre-stage of Alzheimer's, have an increased risk of developing Alzheimer's over the next few years . It is useful and important to diagnose and predict MCI's conversion to Alzheimer's as early as possible for appropriate treatment. In our study, we use numerous machine learning, feature selection as well as clustering methods for this prediction purpose. High precision of prediction is observed for both 10-fold and 2-fold cross-validation. We also use L1 and L2-norm shrinkage terms to control the model complexity. As a result, the prediction error is reduced. These findings illustrate that machine learning methods accurately and reliably predict MCI's conversion, and potentially provide a great assistance to medical diagnosis.