Park Ji-Eun, Mun Sujeong, Lee Siwoo
Future Medicine Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea.
Evid Based Complement Alternat Med. 2021 Feb 8;2021:8315047. doi: 10.1155/2021/8315047. eCollection 2021.
Machine learning may be a useful tool for predicting metabolic syndrome (MetS), and previous studies also suggest that the risk of MetS differs according to Sasang constitution type. The present study investigated the development of MetS prediction models utilizing machine learning methods and whether the incorporation of Sasang constitution type could improve the performance of those prediction models.
Participants visiting a medical center for a health check-up were recruited in 2005 and 2006. Six kinds of machine learning were utilized (K-nearest neighbor, naive Bayes, random forest, decision tree, multilayer perceptron, and support vector machine), as was conventional logistic regression. Machine learning-derived MetS prediction models with and without the incorporation of Sasang constitution type were compared to investigate whether the former would predict MetS with higher sensitivity. Age, sex, education level, marital status, body mass index, stress, physical activity, alcohol consumption, and smoking were included as potentially predictive factors.
A total of 750/2,871 participants had MetS. Among the six types of machine learning methods investigated, multiplayer perceptron and support vector machine exhibited the same performance as the conventional regression method, based on the areas under the receiver operating characteristic curves. The naive-Bayes method exhibited the highest sensitivity (0.49), which was higher than that of the conventional regression method (0.39). The incorporation of Sasang constitution type improved the sensitivity of all of the machine learning methods investigated except for the K-nearest neighbor method.
Machine learning-derived models may be useful for MetS prediction, and the incorporation of Sasang constitution type may increase the sensitivity of such models.
机器学习可能是预测代谢综合征（MetS）的有用工具，先前的研究还表明，根据体质类型，患MetS的风险有所不同。本研究利用机器学习方法研究了MetS预测模型的开发情况，以及纳入体质类型是否能提高这些预测模型的性能。
2005年和2006年招募了到医疗中心进行健康检查的参与者。使用了六种机器学习方法（K近邻、朴素贝叶斯、随机森林、决策树、多层感知器和支持向量机）以及传统逻辑回归。比较了纳入和未纳入体质类型的机器学习衍生的MetS预测模型，以研究前者是否能以更高的敏感性预测MetS。年龄、性别、教育水平、婚姻状况、体重指数、压力、身体活动、饮酒和吸烟被纳入作为潜在的预测因素。
共有750/2871名参与者患有MetS。在所研究的六种机器学习方法中，基于受试者工作特征曲线下的面积，多层感知器和支持向量机表现出与传统回归方法相同的性能。朴素贝叶斯方法表现出最高的敏感性（0.49），高于传统回归方法（0.39）。纳入体质类型提高了除K近邻方法外所有研究的机器学习方法的敏感性。
机器学习衍生的模型可能对MetS预测有用，纳入体质类型可能会提高此类模型的敏感性。
