Exploring Primary and Interaction Effects of Minor Physical Anomalies: Development and Validation of Prediction Models Using Explainable Machine Learning Algorithms for Early-Onset Schizophrenia.

Abstract

Background and hypothesis: Minor physical abnormalities (MPAs) are neurodevelopmental markers that can be traced to prenatal events and may be significant features of early-onset schizophrenia (EOS). Therefore, our study aimed to (1) find the primary and interaction effects of MPAs for EOS and (2) develop and validate the model for EOS based on explainable machine learning algorithms.

Study design: The study included 549 patients with schizophrenia (193 EOS and 356 AOS) and 420 healthy controls (HC) in southern Taiwan. For the feature selection, variable selection using random forests (varSelRF) and recursive feature elimination (RFE) were applied to identify the important variables of MPAs. We used different machine learning algorithms to build the prediction models based on the selected MPAs variables.

Study results: The results showed that the mouth anomalies are significant MPAs variables and have interaction effects with craniofacial MPAs variables for EOS. The prediction models using the selected MPAs variables performed better in discriminating EOS vs HC compared to AOS vs HC. The AUC values for distinguishing EOS vs HC were 0.85-0.93, AOS vs HC were 0.80-0.87, and EOS vs AOS were 0.67-0.77 in validation sets.

Conclusions: This risk prediction model provides a clinical decision support system for detecting patients at high risk of developing EOS and enables early intervention in clinical practice.