Early Alzheimer's disease diagnosis using partial least squares and random forests

Abstract

Currently, the accurate diagnosis of the Alzheimer disease (AD) still remains a challenge in the clinical practice. This paper shows a novel computer aided diagnosis (CAD) system for the early Alzheimer's disease using single photon emission computed tomography (SPECT) images. The proposed system combines a partial least square (PLS) regression model for feature extraction and a random forest predictor. The generalization error of the random forest classifier converges to a limit as the number of trees in the forest increases. PLS feature extraction is found to be more effective for obtaining discriminant information from the data and outperforms principal component analysis (PCA) as a feature extraction technique yielding peak values of sensitivity=100%, specificity= 92.7% and accuracy= 96.9%. Moreover, the proposed CAD system outperformed recently developed AD CAD systems.