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Abstract
Finding effective and objective biomarkers to inform the diagnosis of schizophrenia is of great importance yet remains challenging. Relatively little work has been conducted on multi-biological data for the diagnosis of schizophrenia. In this cross-sectional study, we extracted multiple features from three types of biological data, including gut microbiota data, blood data, and electroencephalogram data. Then, an integrated framework of machine learning consisting of five classifiers, three feature selection algorithms, and four cross validation methods was used to discriminate patients with schizophrenia from healthy controls. Our results show that the support vector machine classifier without feature selection using the input features of multi-biological data achieved the best performance, with an accuracy of 91.7% and an AUC of 96.5% (p < 0.05). These results indicate that multi-biological data showed better discriminative capacity for patients with schizophrenia than single biological data. The top 5% discriminative features selected from the optimal model include the gut microbiota features (Lactobacillus, Haemophilus, and Prevotella), the blood features (superoxide dismutase level, monocyte-lymphocyte ratio, and neutrophil count), and the electroencephalogram features (nodal local efficiency, nodal efficiency, and nodal shortest path length in the temporal and frontal-parietal brain areas). The proposed integrated framework may be helpful for understanding the pathophysiology of schizophrenia and developing biomarkers for schizophrenia using multi-biological data.
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1 South China University of Technology, Department of Biomedical Engineering, School of Material Science and Engineering, Guangzhou, China (GRID:grid.79703.3a) (ISNI:0000 0004 1764 3838); Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China (GRID:grid.79703.3a); South China University of Technology, National Engineering Research Center for Tissue Restoration and Reconstruction, Guangzhou, China (GRID:grid.79703.3a) (ISNI:0000 0004 1764 3838)
2 Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China (GRID:grid.79703.3a); National Engineering Research Center for Healthcare Devices, Guangzhou, China (GRID:grid.79703.3a)
3 New Jersey Institute of Technology, Department of Biomedical Engineering, Newark, USA (GRID:grid.260896.3) (ISNI:0000 0001 2166 4955)
4 The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China (GRID:grid.452505.3) (ISNI:0000 0004 1757 6882); Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China (GRID:grid.452505.3)
5 South China University of Technology, Department of Biomedical Engineering, School of Material Science and Engineering, Guangzhou, China (GRID:grid.79703.3a) (ISNI:0000 0004 1764 3838); The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China (GRID:grid.452505.3) (ISNI:0000 0004 1757 6882); Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China (GRID:grid.452505.3); Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China (GRID:grid.452505.3); South China University of Technology, National Engineering Research Center for Tissue Restoration and Reconstruction, Guangzhou, China (GRID:grid.79703.3a) (ISNI:0000 0004 1764 3838); South China University of Technology, Key Laboratory of Biomedical Engineering of Guangdong Province, Guangzhou, China (GRID:grid.79703.3a) (ISNI:0000 0004 1764 3838); National Engineering Research Center for Healthcare Devices, Guangzhou, China (GRID:grid.79703.3a); Tohoku University, Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Sendai, Japan (GRID:grid.69566.3a) (ISNI:0000 0001 2248 6943)