Machine learning for prediction of schizophrenia using genetic and demographic factors in the UK biobank.

ABSTRACT: Machine learning (ML) holds promise for precision psychiatry, but its predictive performance is unclear. We assessed whether ML provided added value over logistic regression for prediction of schizophrenia, and compared models built using polygenic risk scores (PRS) or clinical/demographic factors. LASSO and ridge-penalised logistic regression, support vector machines (SVM), random forests, boosting, neural networks and stacked models were trained to predict schizophrenia, using PRS for schizophrenia (PRS_SZ), sex, parental depression, educational attainment, winter birth, handedness and number of siblings as predictors. Models were evaluated for discrimination using area under the receiver operator characteristic curve (AUROC) and relative importance of predictors using permutat

SUBMITTER: Bracher-Smith M 

PROVIDER: S-EPMC9399753 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature