Detection of diabetic patients in people with normal fasting glucose using machine learning

BMC Medicine

Research Article

Dong Zhao¹  Jing Ke¹  Qinghua Cui²  Chunmei Cui²  Rui Fan²  Lina Zhang³  Pengyu Wang⁴  Liming Yang⁵  Kun Lv⁶  Xiaojuan Zha⁷  Jun Zhang⁸ 

[1] Beijing Key Laboratory of Diabetes Research and Care, Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, People’s Republic of China;Department of Biomedical Informatics, State Key Laboratory of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, Beijing, People’s Republic of China;Department of Laboratory Diagnosis, Daqing Oil Field General Hospital, Daqing, People’s Republic of China;Department of Pathophysiology, Harbin Medical University, Harbin, People’s Republic of China;Department of Pathophysiology, Harbin Medical University, Harbin, People’s Republic of China;National Key Laboratory of Frigid Zone Cardiovascular Diseases (NKLFZCD), Harbin Medical University, Harbin, People’s Republic of China;NHC Key Laboratory of Cell Transplantation, The First Affiliated Hospital of Harbin Medical University, Harbin, People’s Republic of China;Key Laboratory of Non-Coding RNA Transformation Research of Anhui Higher Education Institutes, Wuhu, China;Central Laboratory, First Affiliated Hospital of Wannan Medical College, Wuhu, People’s Republic of China;Laboratory Medicine, First Affiliated Hospital of Wannan Medical College, Wuhu, People’s Republic of China;Medical College of Shihezi University, Shihezi, People’s Republic of China;

关键词: Diabetes risk prediction;    Normal fasting glucose;    Machine learning;    Missed diagnosis;   

DOI  :  10.1186/s12916-023-03045-9

 received in 2023-01-20, accepted in 2023-08-23,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundDiabetes mellitus (DM) is a chronic metabolic disease that could produce severe complications threatening life. Its early detection is thus quite important for the timely prevention and treatment. Normally, fasting blood glucose (FBG) by physical examination is used for large-scale screening of DM; however, some people with normal fasting glucose (NFG) actually have suffered from diabetes but are missed by the examination. This study aimed to investigate whether common physical examination indexes for diabetes can be used to identify the diabetes individuals from the populations with NFG.MethodsThe physical examination data from over 60,000 individuals with NFG in three Chinese cohorts were used. The diabetes patients were defined by HbA1c ≥ 48 mmol/mol (6.5%). We constructed the models using multiple machine learning methods, including logistic regression, random forest, deep neural network, and support vector machine, and selected the optimal one on the validation set. A framework using permutation feature importance algorithm was devised to discover the personalized risk factors.ResultsThe prediction model constructed by logistic regression achieved the best performance with an AUC, sensitivity, and specificity of 0.899, 85.0%, and 81.1% on the validation set and 0.872, 77.9%, and 81.0% on the test set, respectively. Following feature selection, the final classifier only requiring 13 features, named as DRING (diabetes risk of individuals with normal fasting glucose), exhibited reliable performance on two newly recruited independent datasets, with the AUC of 0.964 and 0.899, the balanced accuracy of 84.2% and 81.1%, the sensitivity of 100% and 76.2%, and the specificity of 68.3% and 86.0%, respectively. The feature importance ranking analysis revealed that BMI, age, sex, absolute lymphocyte count, and mean corpuscular volume are important factors for the risk stratification of diabetes. With a case, the framework for identifying personalized risk factors revealed FBG, age, and BMI as significant hazard factors that contribute to an increased incidence of diabetes. DRING webserver is available for ease of application (http://www.cuilab.cn/dring).ConclusionsDRING was demonstrated to perform well on identifying the diabetes individuals among populations with NFG, which could aid in early diagnosis and interventions for those individuals who are most likely missed.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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