Tensor factorization-based classification of Alzheimer´s disease vs healthy controls

So far there is no accurate diagnosis and effective treatment for Alzheimer´s disease (AD), which is the most common neurodegenerative illness. Earlier diagnosis and prevention for AD are important goals for many researchers. In this paper, we propose a computational model, based on Tensor decomposition such as the Non-negative Multi-way Factorization (NMWF), to deal with structural magnetic resonance images (sMRI) data of 126 AD patients and 179 healthy controls (HC) at two time points of baseline and one-year. The NMWF algorithm is applied to sMRI data tensor with five factors of 3D tensor-sMRI, patient, and time to find the sMRI representation basis. The fourth and fifth factors then are fed into a classifier based on support vector machine to classify MR images of AD and HC. The experimental results on the sMRI data showed that NMWF-based method has well feasibility for discerning AD from HC from ADNI database.