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

目前的研究项目旨在开发一种机器学习（ML）模型来预测越南农村中年人中未确诊的糖尿病（DM），有三个具体目标：1)开发用于DM预测的ML模型；2)外部验证DM预测模型；3)利用外部验证模型开发DM预测的计算机辅助工具。在2021财年，我专注于完成第一个研究项目的目标。简而言之，在越南中部以人群为基础的心血管疾病队列研究Khanh Hoa心血管研究（KHCS）中，基线数据（n = 3000）被用于模型开发。对四种候选机器学习算法（Logistic回归[LR]、支持向量分类[SVC]、线性SVC和随机梯度下降[SGD]）的预测性能进行了评估，其中SVC表现出最高的预测性能（AUC: 71.7;95% CI: 65.0, 78.2）和最佳校准。这些模型使用非侵入性预测因子，即人口统计学（年龄和性别）、人体测量学（如腰高比）、生活方式（如体力活动）和饮食（如水果和蔬菜消费）变量。然而，自2022年8月1日起，我离开了日本，不再为国家全球卫生与医学中心（NCGM）工作。因此，该项目的继续是不可行的，因此，该项目已经终止。

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.