Performance of candidate models.

Metabolic syndrome (MetS) is a chronic disease caused by obesity, high blood pressure, high blood sugar, and dyslipidemia and may lead to cardiovascular disease or type 2 diabetes. Therefore, the detection and prevention of MetS at an early stage are imperative. Individuals can detect MetS early and manage it effectively if they can easily monitor their health status in their daily lives. In this study, a predictive model for MetS was developed utilizing solely noninvasive information, thereby facilitating its practical application in real-world scenarios. The model’s construction deliberately excluded three features requiring blood testing, specifically those for triglycerides, blood sugar, and HDL cholesterol. We used a large-scale Korean health examination dataset (n = 70, 370; the prevalence of MetS = 13.6%) to develop the predictive model. To obtain informative features, we developed three novel synthetic features from four basic information: waist circumference, systolic and diastolic blood pressure, and gender. We tested several classification algorithms and confirmed that the decision tree model is the most appropriate for the practical prediction of MetS. The proposed model achieved good performance, with an AUC of 0.889, a recall of 0.855, and a specificity of 0.773. It uses only four base features, which results in simplicity and easy interpretability of the model. In addition, we performed calibrations on the prediction probability and calibrated the model. Therefore, the proposed model can provide MetS diagnosis and risk prediction results. We also proposed a MetS risk map such that individuals could easily determine whether they had metabolic syndrome.代谢综合征（Metabolic Syndrome，简称MetS）是一类由肥胖、高血压、高血糖及血脂异常诱发的慢性疾病，可进展为心血管疾病或2型糖尿病。因此，早期检出与防控MetS具有重要意义。若个体能够在日常生活中便捷监测自身健康状态，便可实现MetS的早期发现与有效管理。本研究仅采用无创信息构建了MetS预测模型，以推动其在真实场景中的落地应用。该模型构建过程中刻意剔除了三项需血液检测的特征，分别为甘油三酯、血糖及高密度脂蛋白胆固醇（HDL Cholesterol）。本研究使用大规模韩国健康检查数据集（样本量n=70370，MetS患病率为13.6%）开展预测模型构建。为获取有效特征，本研究基于四项基础信息——腰围、收缩压、舒张压及性别，生成了三项全新的人工合成特征。本研究测试了多种分类算法，证实决策树模型最适用于MetS的实用化预测。所提模型展现出优异性能，受试者工作特征曲线下面积（Area Under the Receiver Operating Characteristic Curve，简称AUC）为0.889，召回率为0.855，特异度为0.773。该模型仅依赖四项基础特征，因此结构简洁且具备良好的可解释性。此外，本研究针对预测概率开展了校准操作，并完成了模型校准。因此，所提模型可输出MetS诊断及风险预测结果。本研究还提出了MetS风险图谱，便于个体便捷地自我判断是否罹患代谢综合征。