Development of a computer-assisted predictive tool for facilitating selective screening for undiagnosed diabetes mellitus in Vietnam

本研究项目旨在开发一个机器学习(ML)模型来预测越南农村地区的中年糖尿病(DM)，具体目标有三：1)开发DM预测的ML模型；2)外部验证所提出的DM预测模型；3)使用外部验证的模型开发计算机辅助DM预测工具。在2021财年，我专注于实现第一个研究项目的目标。简单地说，清华心血管研究(KHCS)的基线数据(n=3000)被用于模型开发，KHCS是一项基于人群的越南中部心血管疾病队列研究。评估了四种候选最大似然算法(Logistic回归[LR]、支持向量分类[SVC]、线性SVC和随机梯度下降[SGD])的预测性能，其中SVC表现出最高的性能(AUC：71.7；95%CI，65.0，78.2)和最好的校准。这些模型使用了非侵入性的预测因子，即人口(年龄和性别)、人体测量(例如腰围与身高比)、生活方式(例如体力活动)和饮食(例如水果和蔬菜的摄入量)变量。然而，自2022年8月1日以来，我已经搬出日本，不再为国家全球卫生与医学中心(NCGM)工作。因此，继续该项目是不可行的，因此，该项目已被终止。

The present research project aims to develop a machine learning (ML) model to predict undiagnosed diabetes mellitus (DM) among middle-aged adults in rural Vietnam, with 3 specific objectives: 1) to develop a ML model for DM prediction; 2) to externally validate the proposed DM predictive model; and 3) to develop a computer-assisted tool for DM prediction, using the externally validated model.During the fiscal year of 2021, I focused on the fulfillment of the first research project's objective. Briefly, the baseline data (n = 3000) of the Khanh Hoa cardiovascular study (KHCS), a population-based cohort study on cardiovascular disease in the Central Vietnam, were used for the model development. Four candidate ML algorithms (Logistic regression [LR], support vector classification [SVC], Linear SVC, and stochastic gradient descent [SGD]) were evaluated for their predictive performance, among them SVC showed the highest performance (AUC: 71.7 ;95% CI, 65.0, 78.2) and the best calibration. The models used non-invasive predictors, i.e., demographic (age, and sex), anthropometric (e.g., waist-to-height ratio), lifestyle (e.g., physical activity), and dietary (e.g., fruit and vegetable consumption) variables.However, since the 1st of August 2022, I have moved out of Japan, and no longer work for the National Center for Global Health and Medicine (NCGM). Because of this, the continuation of the project is not feasible, and thus, the project has been terminated.