SSGL1/2: An Improved SVM with Smooth GroupL1/2 for Predicting AD

Abstract

Alzheimer’s disease (AD) is currently one of the mainstream senile diseases recognized in the world. It is the key problem how to automatically identify the early AD based on structed Magnetic Resonance Imaging (sMRI). In order to achieve accurate recognition of AD and obtain highly relevant brain lesions, an improved SVM with group L1/2 sparse regularization and smoothing function (SGL1/2) is proposed. It can achieve sparseness within the group, and approximate the non-smooth absolute value function to a smooth function. The improved model adopts a calibrated hinge to replace the hinge loss function in traditional SVM which is abbreviated as SSGL1/2. In the experiment, the proposed model is applied to different sMRI datasets for training and testing. Compared to other regularization of the non-group level and the group level, the classification accuracy of the proposed method reaches up to 96.03%. At the same time, the algorithm can point out the important brain areas in the MRI group, which has important reference value for the doctor’s predictive work.