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

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 the model-based predictors explain self-reported persecutory beliefs, identified the most relevant predictors, and investigated their specificity in explaining persecutory beliefs as opposed to delusional beliefs or psychopathological symptoms in general. The machine learning model explained 31% of the variance in persecutory beliefs, 47% of delusions in general, and 77% of general psychopathology. The ten predictors with the most influence on predicting persecutory beliefs included negative beliefs about mistrust, cognitive fusion, ostracism, threat anticipation, generalised negative other beliefs, trust, aberrant salience, hallucinations, stress, and emotion regulation difficulties. The limited explanatory power of the proposed predictors raises questions about the validity of existing models and suggests that crucial predictors specific to persecutory delusions may be missing. Our findings highlight the importance of investigating, refining, and cross-validating theoretical aetiological models to improve our understanding of the aetiology of delusions.