Nonlinear feature space transformation to improve the prediction of MCI to AD conversion

Abstract

Accurate identification of patients with Mild Cognitive Impairment (MCI) at high risk for conversion to Alzheimer’s Disease (AD) offers an opportunity to target the disease process early. In this paper, we present a novel nonlinear feature transformation scheme to improve the prediction of MCI-AD conversion through semi-supervised learning. Utilizing Laplacian SVM (LapSVM) as a host classifier, the proposed method learns a smooth spatially varying transformation that makes the input data more linearly separable. Our approach has a broad applicability to boost the classification performance of many other semi-supervised learning solutions. Using baseline MR images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, we evaluate the effectiveness of the proposed semi-supervised framework and demonstrate the improvements over the state-of-the-art solutions within the same category.