Kim Hyerim, Heo Ji Hye, Lim Dong Hoon, Kim Yoona
Department of Food and Nutrition, Gyeongsang National University, Jinju 52828, Korea.
Department of Information & Statistics, Gyeongsang National University, Jinju 52828, Korea.
Clin Nutr Res. 2023 Apr 25;12(2):138-153. doi: 10.7762/cnr.2023.12.2.138. eCollection 2023 Apr.
The prevalence of metabolic syndrome (MetS) and its cost are increasing due to lifestyle changes and aging. This study aimed to develop a deep neural network model for prediction and classification of MetS according to nutrient intake and other MetS-related factors. This study included 17,848 individuals aged 40-69 years from the Korea National Health and Nutrition Examination Survey (2013-2018). We set MetS (3-5 risk factors present) as the dependent variable and 52 MetS-related factors and nutrient intake variables as independent variables in a regression analysis. The analysis compared and analyzed model accuracy, precision and recall by conventional logistic regression, machine learning-based logistic regression and deep learning. The accuracy of train data was 81.2089, and the accuracy of test data was 81.1485 in a MetS classification and prediction model developed in this study. These accuracies were higher than those obtained by conventional logistic regression or machine learning-based logistic regression. Precision, recall, and F1-score also showed the high accuracy in the deep learning model. Blood alanine aminotransferase (β = 12.2035) level showed the highest regression coefficient followed by blood aspartate aminotransferase (β = 11.771) level, waist circumference (β = 10.8555), body mass index (β = 10.3842), and blood glycated hemoglobin (β = 10.1802) level. Fats (cholesterol [β = -2.0545] and saturated fatty acid [β = -2.0483]) showed high regression coefficients among nutrient intakes. The deep learning model for classification and prediction on MetS showed a higher accuracy than conventional logistic regression or machine learning-based logistic regression.
由于生活方式的改变和老龄化，代谢综合征（MetS）的患病率及其成本正在上升。本研究旨在根据营养摄入和其他与MetS相关的因素，开发一种用于MetS预测和分类的深度神经网络模型。本研究纳入了韩国国家健康与营养检查调查（2013 - 2018年）中17848名40 - 69岁的个体。在回归分析中，我们将MetS（存在3 - 5个风险因素）设为因变量，将52个与MetS相关的因素和营养摄入变量设为自变量。该分析通过传统逻辑回归、基于机器学习的逻辑回归和深度学习比较并分析了模型的准确性、精确率和召回率。在本研究开发的MetS分类和预测模型中，训练数据的准确率为81.2089，测试数据的准确率为81.1485。这些准确率高于通过传统逻辑回归或基于机器学习的逻辑回归所获得的准确率。精确率、召回率和F1分数在深度学习模型中也显示出较高的准确性。血液丙氨酸氨基转移酶（β = 12.2035）水平显示出最高的回归系数，其次是血液天冬氨酸氨基转移酶（β = 11.771）水平、腰围（β = 10.8555）、体重指数（β = 10.3842）和血液糖化血红蛋白（β = 10.1802）水平。在营养摄入中，脂肪（胆固醇[β = -2.0545]和饱和脂肪酸[β = -2.0483]）显示出较高的回归系数。用于MetS分类和预测的深度学习模型显示出比传统逻辑回归或基于机器学习的逻辑回归更高的准确性。
