Machine learning for prediction of schizophrenia based on identifying the primary and interaction effects of minor physical anomalies

Shuen Lin Jeng, Ming Jun Tu, Chih Wei Lin, Jin Jia Lin, Huai Hsuan Tseng, Fong Lin Jang, Ming Kun Lu, Po See Chen, Chih Chun Huang, Wei Hung Chang, Hung Pin Tan, Sheng Hsiang Lin

研究成果: Article同行評審

摘要

In the neurodevelopmental model of schizophrenia, minor physical anomalies (MPAs) are considered neurodevelopmental markers of schizophrenia. To date, there has been no research to evaluate the interaction between MPAs. Our study built and used a machine learning model to predict the risk of schizophrenia based on measurements of MPA items and to investigate the potential primary and interaction effects of MPAs. The study included 470 patients with schizophrenia and 354 healthy controls. The models used are classical statistical model, Logistic Regression (LR), and machine leaning models, Decision Tree (DT) and Random Forest (RF). We also plotted two-dimensional scatter diagrams and three-dimensional linear/quadratic discriminant analysis (LDA/QDA) graphs for comparison with the DT dendritic structure. We found that RF had the highest predictive power for schizophrenia (Full-training AUC = 0.97 and 5-fold cross-validation AUC = 0.75). We identified several primary MPAs, such as the mouth region, high palate, furrowed tongue, skull height and mouth width. Quantitative MPA analysis indicated that the higher skull height and the narrower mouth width, the higher the risk of schizophrenia. In the interaction, we further identified that skull height and mouth width, furrowed tongue and skull height, high palate and skull height, and high palate and furrowed tongue, showed significant two-item interactions with schizophrenia. A weak three-item interaction was found between high palate, skull height, and mouth width. In conclusion, we found that the two machine learning methods showed good predictive ability in assessing the risk of schizophrenia using the primary and interaction effects of MPAs.

原文English
頁(從 - 到)108-118
頁數11
期刊Journal of Psychiatric Research
172
DOIs
出版狀態Published - 2024 4月

All Science Journal Classification (ASJC) codes

  • 精神病學和心理健康
  • 生物精神病學

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