RESEARCH ON CID PATIENT CLASSIFICATION BASED ON MULTIMODAL FEATURE INTEGRATION ALGORITHM

At present, the number of patients withchronic insomnia disorder (CID) is increasing year by year. Timely diagnosis can effectively avoid the aggravation of symptoms of CID patients. Magnetic resonance imaging (MRI) technology combined with a classification algorithm can be used to identify CID patients. The traditional MRI data classification algorithm is based on single-mode feature SVM algorithm, but this algorithm has poor effect on CID patient data classification. Therefore, a CID patient recognition algorithm based on multimodal feature integration is proposed to achieve better results. The multimodal feature integration algorithm mapped multimodal features based on resting-state functional MRI technology and used the integration algorithm for classification and comparison experiments. The experimental results show that, compared with the traditional MRI classification algorithm, the multimodal feature integration algorithm has better classification effect on CID patient data, and can effectively identify CID patients, to carry out relevant medical auxiliary diagnosis.