An integrated machine learning framework for a discriminative analysis of schizophrenia using multi-biological data.

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 performanc

SUBMITTER: Ke PF 

PROVIDER: S-EPMC8290033 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature