Department of Endocrine Metabolism, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
Department of General Practice, School of Medicine, Institute of Diabetes, Zhongda Hospital, Southeast University, Nanjing, China.
J Diabetes Investig. 2024 Jun;15(6):743-750. doi: 10.1111/jdi.14166. Epub 2024 Mar 4.
AIMS/INTRODUCTION: Machine learning algorithms based on the artificial neural network (ANN), support vector machine, naive Bayesian or logistic regression model are commonly used to identify diabetes. This study investigated which approach performed the best and whether muscle strength provided any incremental benefit in identifying undiagnosed diabetes in Chinese adults.
This cross-sectional study enrolled 4,482 eligible participants from eight provinces in China, who were randomly divided into the training dataset (n = 3,586) and the testing dataset (n = 896). Muscle strength was assessed by handgrip strength and the number of chair stands in the 30-s chair stand test. An oral glucose tolerance test was used to ascertain undiagnosed diabetes. The areas under the curve (AUCs) were calculated accordingly and compared with each other.
Of the included participants, 233 had newly diagnosed diabetes. All the four machine learning algorithms, which were developed based on nonlaboratory parameters, showed acceptable discriminative ability in identifying undiagnosed diabetes (all AUCs >0.70), with the ANN approach performing the best (AUC 0.806). Adding handgrip strength or the 30-s chair stand test to this approach did not increase the AUC further (P = 0.39 and 0.26, respectively). Furthermore, compared with the New Chinese Diabetes Risk Score, the ANN approach showed a larger AUC in identifying undiagnosed diabetes (P < 0.01), regardless of the addition of handgrip strength or the 30-s chair stand test.
The ANN approach performed the best in identifying undiagnosed diabetes in Chinese adults; however, the addition of muscle strength might not improve its efficacy.
目的/引言：基于人工神经网络（ANN）、支持向量机、朴素贝叶斯或逻辑回归模型的机器学习算法常用于识别糖尿病。本研究旨在探究哪种方法效果最佳，以及肌肉力量是否能提高识别中国成年人未确诊糖尿病的能力。
本横断面研究纳入了来自中国 8 个省份的 4482 名符合条件的参与者，他们被随机分为训练数据集（n=3586）和测试数据集（n=896）。肌肉力量通过握力和 30 秒椅站测试中的椅站次数来评估。口服葡萄糖耐量试验用于确定未确诊的糖尿病。相应地计算了曲线下面积（AUCs）并进行了比较。
在纳入的参与者中，有 233 人患有新诊断的糖尿病。所有四种基于非实验室参数开发的机器学习算法在识别未确诊糖尿病方面均表现出可接受的区分能力（所有 AUC 均>0.70），其中 ANN 方法表现最佳（AUC 为 0.806）。将握力或 30 秒椅站测试添加到此方法中并没有进一步提高 AUC（P=0.39 和 0.26，分别）。此外，与新的中国糖尿病风险评分相比，无论是否添加握力或 30 秒椅站测试，ANN 方法在识别未确诊糖尿病方面均显示出更大的 AUC（P<0.01）。
ANN 方法在识别中国成年人未确诊糖尿病方面表现最佳；然而，肌肉力量的加入可能不会提高其效果。
