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Exploring Primary and Interaction Effects of Minor Physical Anomalies: Development and Validation of Prediction

Chih-Wei Lin1,2, Jin-Jia Lin3, Huai-Hsuan Tseng4

  • 1Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan 704302, Taiwan.

Schizophrenia Bulletin
|April 3, 2025
PubMed
Abstract

Insights

Minor physical abnormalities, particularly mouth anomalies, are key indicators for early-onset schizophrenia (EOS). Machine learning models using these markers effectively predict EOS risk, aiding early intervention.

Area of Science:

  • Neurodevelopmental disorders
  • Psychiatric research
  • Biomedical informatics

Background:

  • Minor physical abnormalities (MPAs) are prenatal neurodevelopmental markers.
  • MPAs may be significant indicators of early-onset schizophrenia (EOS).

Purpose of the Study:

  • Investigate the primary and interaction effects of MPAs in EOS.
  • Develop and validate an explainable machine learning model for EOS prediction using MPAs.

Main Methods:

  • Utilized random forests (varSelRF) and recursive feature elimination (RFE) for MPA variable selection.
  • Developed prediction models using machine learning algorithms based on selected MPAs.
  • Included 549 schizophrenia patients (193 EOS, 356 AOS) and 420 healthy controls (HC).

Main Results:

  • Mouth anomalies identified as significant MPAs with interaction effects for EOS.
  • Machine learning models demonstrated superior discrimination for EOS vs. HC (AUC 0.85-0.93) compared to AOS vs. HC (AUC 0.80-0.87).
  • Models also distinguished EOS from AOS (AUC 0.67-0.77).

Conclusions:

  • Developed a risk prediction model for early-onset schizophrenia.
  • The model serves as a clinical decision support system for early detection and intervention.
  • Highlights the clinical utility of MPAs in identifying individuals at high risk for EOS.
Keywords:
early-onset schizophreniainteraction effectsmachine learning algorithmsminor physical anomalies

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