Department of Statistics, Institute of Data Science, and Center for Innovative FinTech Business Models, National Cheng Kung University, Tainan, Taiwan.
Department of Statistics, National Cheng Kung University, Tainan, Taiwan.
J Psychiatr Res. 2024 Apr;172:108-118. doi: 10.1016/j.jpsychires.2024.02.032. Epub 2024 Feb 14.
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.
在精神分裂症的神经发育模型中，微小的身体异常（MPAs）被认为是精神分裂症的神经发育标志物。迄今为止，还没有研究评估 MPAs 的相互作用。我们的研究构建并使用了一个机器学习模型，根据 MPA 项目的测量值来预测精神分裂症的风险，并研究 MPAs 的潜在主要和相互作用效应。该研究纳入了 470 名精神分裂症患者和 354 名健康对照者。使用的模型是经典统计学模型、逻辑回归（LR）和机器学习模型、决策树（DT）和随机森林（RF）。我们还绘制了二维散点图和三维线性/二次判别分析（LDA/QDA）图，以便与 DT 树突结构进行比较。我们发现 RF 对精神分裂症具有最高的预测能力（全训练 AUC=0.97，5 折交叉验证 AUC=0.75）。我们确定了几个主要的 MPA，如口腔区域、高腭、沟纹舌、颅高和口宽。定量 MPA 分析表明，颅高越高，口越窄，患精神分裂症的风险越高。在相互作用中，我们进一步确定了颅高和口宽、沟纹舌和颅高、高腭和颅高以及高腭和沟纹舌之间存在显著的二项相互作用与精神分裂症有关。发现高腭、颅高和口宽之间存在微弱的三项相互作用。总之，我们发现这两种机器学习方法在使用 MPA 的主要和相互作用效应评估精神分裂症风险方面表现出良好的预测能力。
