Multi RBF-kernel support vector regression for clinical cognitive scores prediction in schizophrenia

Abstract

This article aims to design a regression model to successfully predict clinical scores based on features provided by multimodal magnetic resonance (MR) images. We propose a multimodal MR predictive analysis pipeline with nonlinear support vector regression (SVR). Firstly, feature selection methods are elaborately chosen according to different feature characteristics for each modality. For features extracted from structural MR images and diffusion MR images, Support Vector Machine-Recursive Feature Elimination with Correlation Bias Reduction (SVMRFE+CBR) is applied to select predictive features and eliminate the high intra-correlations exist in the original features. For functional features, sparse coding (SC) considers multiple features’ combination for group predictiveness but cost less computation. After feature selection, the single radial bias function (RBF) kernels are calculated based on the modalities’ respectively selected features. A multi-RBF kernel is calculated by weighted-sum of single RBF kernels, and the kernel finally serves for the multimodal SVR model. Our proposed framework is tested on an online schizophrenia (SZ) dataset with 171 subjects from two study cohorts using 10-fold cross-validation (CV). The models are trained by the subjects’ clinicalrelated scores in Positive and Negative Syndrome Scale (PANSS) to be able to give the estimated scores as precisely as possible. The experimental results show that our proposed model can successfully predict clinical scores. Further comparative test results show that the proposed multimodal model can improve predictiveness compared with single modal ones, and our choice of feature selection methods plays an important role in the good performance.