Integrated Prediction Method for Mental Illness with Multimodal Sleep Function Indicators

Abstract

Sleep quality has great effect on physical and mental health. Severe insomnia will cause autonomic neurological dysfunction. For making good clinical decisions, it is crucial to extract features of sleep quality and accurately predict the mental illness. Prior studies have a number of deficiencies to be overcome. On the one hand, the selected features for sleep quality are not good enough, as they do not account for multisource and heterogeneous features. On the other hand, the mental illness prediction model does not work well and thus needs to be enhanced and improved. This paper presents a multi-dimensional feature extraction method and an ensemble prediction model for mental illness. First, we do correlation analysis for every indicators and sleep quality, and further select the optimal heterogeneous features. Next, we propose a combinational model, which is integrated by basic modules according to their weights. Finally, we perform abundant experiments to test our method. Experimental results demonstrate that our approach outperforms many state-of-the-art approaches.