Abstract：
BACKGROUND: Metabolic syndrome (MetS) is considered to be an important parameter of cardio-metabolic health and contributing to the development of atherosclerosis, type 2 diabetes. The incidence of MetS significantly increases in postmenopausal women, therefore, the perimenopausal period is considered a critical phase for prevention. We aimed to use four machine learning methods to predict whether perimenopausal women will develop MetS within 2 years.
METHODS: Women aged 45-55 years who underwent 2 consecutive years of physical examinations in Ninth Clinical College of Peking University between January 2021 and December 2022 were included. We extracted 26 features from physical examinations, and used backward selection method to select top 10 features with the largest area under the receiver operating characteristic curve (AUC). Extreme gradient boosting (XGBoost), Random forest (RF), Multilayer perceptron (MLP) and Logistic regression (LR) were used to establish the model. Those performance were measured by AUC, accuracy, precision, recall and F1 score. SHapley Additive exPlanation (SHAP) value was used to identify risk factors affecting perimenopausal MetS.
RESULTS: A total of 8700 women had physical examination records, and 2,254 women finally met the inclusion criteria. For predicting MetS events, RF and XGBoost had the highest AUC (0.96, 0.95, respectively). XGBoost has the highest F1 value (F1 = 0.77), followed by RF, LR and MLP. SHAP value suggested that the top 5 variables affecting MetS in this study were Waist circumference, Fasting blood glucose, High-density lipoprotein cholesterol, Triglycerides and Diastolic blood pressure, respectively.
CONCLUSIONS: We\'ve developed a targeted MetS risk prediction model for perimenopausal women, using health examination data. This model enables early identification of high MetS risk in this group, offering significant benefits for individual health management and wider socio-economic health initiatives.
METHODS: Women aged 45-55 years who underwent 2 consecutive years of physical examinations in Ninth Clinical College of Peking University between January 2021 and December 2022 were included. We extracted 26 features from physical examinations, and used backward selection method to select top 10 features with the largest area under the receiver operating characteristic curve (AUC). Extreme gradient boosting (XGBoost), Random forest (RF), Multilayer perceptron (MLP) and Logistic regression (LR) were used to establish the model. Those performance were measured by AUC, accuracy, precision, recall and F1 score. SHapley Additive exPlanation (SHAP) value was used to identify risk factors affecting perimenopausal MetS.
RESULTS: A total of 8700 women had physical examination records, and 2,254 women finally met the inclusion criteria. For predicting MetS events, RF and XGBoost had the highest AUC (0.96, 0.95, respectively). XGBoost has the highest F1 value (F1 = 0.77), followed by RF, LR and MLP. SHAP value suggested that the top 5 variables affecting MetS in this study were Waist circumference, Fasting blood glucose, High-density lipoprotein cholesterol, Triglycerides and Diastolic blood pressure, respectively.
CONCLUSIONS: We\'ve developed a targeted MetS risk prediction model for perimenopausal women, using health examination data. This model enables early identification of high MetS risk in this group, offering significant benefits for individual health management and wider socio-economic health initiatives.
摘要：
背景：代谢综合征（MetS）被认为是心脏代谢健康的重要参数，并有助于动脉粥样硬化的发展，2型糖尿病。绝经后妇女的MetS发病率显著增加，因此,围绝经期被认为是预防的关键阶段.我们的目的是使用四种机器学习方法来预测围绝经期妇女在2年内是否会发生MetS。
方法：纳入2021年1月至2022年12月在北京大学第九临床学院连续2年体检的45-55岁女性。我们从体检中提取了26个特征，并使用反向选择方法选择接收器工作特征曲线（AUC）下面积最大的前10个特征。极端梯度提升（XGBoost），随机森林(RF)，采用多层感知器(MLP)和Logistic回归(LR)建立模型。这些性能是通过AUC来衡量的，准确度,精度,召回和F1得分。采用SHAP值确定影响围绝经期MetS的危险因素。
结果：共有8700名妇女有体检记录，2,254名女性最终符合纳入标准。为了预测MetS事件，RF和XGBoost具有最高的AUC(分别为0.96、0.95)。XGBoost具有最高的F1值（F1=0.77），其次是RF，LR和MLP。SHAP值表明，本研究中影响MetS的前5个变量是腰围，空腹血糖,高密度脂蛋白胆固醇，甘油三酯和舒张压，分别。
结论：我们开发了针对围绝经期妇女的有针对性的MetS风险预测模型，使用健康体检数据。该模型可以早期识别该组中的高MetS风险，为个人健康管理和更广泛的社会经济健康举措提供显著的好处。
方法：纳入2021年1月至2022年12月在北京大学第九临床学院连续2年体检的45-55岁女性。我们从体检中提取了26个特征，并使用反向选择方法选择接收器工作特征曲线（AUC）下面积最大的前10个特征。极端梯度提升（XGBoost），随机森林(RF)，采用多层感知器(MLP)和Logistic回归(LR)建立模型。这些性能是通过AUC来衡量的，准确度,精度,召回和F1得分。采用SHAP值确定影响围绝经期MetS的危险因素。
结果：共有8700名妇女有体检记录，2,254名女性最终符合纳入标准。为了预测MetS事件，RF和XGBoost具有最高的AUC(分别为0.96、0.95)。XGBoost具有最高的F1值（F1=0.77），其次是RF，LR和MLP。SHAP值表明，本研究中影响MetS的前5个变量是腰围，空腹血糖,高密度脂蛋白胆固醇，甘油三酯和舒张压，分别。
结论：我们开发了针对围绝经期妇女的有针对性的MetS风险预测模型，使用健康体检数据。该模型可以早期识别该组中的高MetS风险，为个人健康管理和更广泛的社会经济健康举措提供显著的好处。
