Using machine learning to predict persecutory beliefs based on aetiological models of delusions identified in a systematic literature search.

ABSTRACT: Aetiological models of delusions propose a broad range of predictors. The extent to which these predictors explain variance in persecutory beliefs across the continuum requires systematic investigation. As part of a previous review, 51 aetiological models of delusions were identified in a systematic literature search using PubMed, Web of Science, and Science Direct databases. Omitting repetitions, 66 unique postulated predictors of delusions and persecutory delusions were extracted from these models, of which 55 met our inclusion criteria and were assessed in a cross-sectional online sample stratified by delusion severity (N = 336) using self-report and behavioural measures. Utilising machine learning (i.e., random forests with nested cross-validation), we investigated the extent to which

SUBMITTER: Denecke S 

PROVIDER: S-EPMC12479767 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature