Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework

• Published in: Journal of diabetes research 2020-09, Vol.2020 (2020), p.1-12
• Main Authors: Wang, Shuxia, Li, Chen, Su, Yinxia, Xue, Mingyue, Yao, Hua
• Format: Article
• Language: English
• Subjects: Abdomen; Adult; Age; Aged; Alcohol Drinking; Algorithms; Area Under Curve; Asians; Blood pressure; Blood tests; Body mass index; Cardiovascular disease; China - epidemiology; China - ethnology; Decision Making; Decision Trees; Diabetes; Diabetes Mellitus, Type 2 - diagnosis; Diabetes Mellitus, Type 2 - epidemiology; Diabetes Mellitus, Type 2 - ethnology; Diet; Eating behavior; Ethnicity; Female; Gender; Glucose; Humans; Hypertension; Lifestyles; Logistic Models; Machine Learning; Male; Medical laboratories; Medically Underserved Area; Middle Aged; Normal distribution; Odds Ratio; Questionnaires; Regression Analysis; Risk Factors; Smoking; Surveys and Questionnaires; Ultrasonic imaging; Variables; Waist Circumference
• ISSN: 2314-6745; 2314-6753
• DOI: 10.1155/2020/6873891

Background. An estimated 425 million people globally have diabetes, accounting for 12% of the world’s health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas. Methods. A total of 584,168 adult subjects who have participated in the national physical examination were enrolled in this study. The risk factors for type II diabetes mellitus (T2DM) were identified by p values and odds ratio, using logistic regression (LR) based on variables of physical measurement and a questionnaire. Combined with the risk factors selected by LR, we used a decision tree, a random forest, AdaBoost with a decision tree (AdaBoost), and an extreme gradient boosting decision tree (XGBoost) to identify individuals with T2DM, compared the performance of the four machine learning classifiers, and used the best-performing classifier to output the degree of variables’ importance scores of T2DM. Results. The results indicated that XGBoost had the best performance (accuracy=0.906, precision=0.910, recall=0.902, F‐1=0.906, and AUC=0.968). The degree of variables’ importance scores in XGBoost showed that BMI was the most significant feature, followed by age, waist circumference, systolic pressure, ethnicity, smoking amount, fatty liver, hypertension, physical activity, drinking status, dietary ratio (meat to vegetables), drink amount, smoking status, and diet habit (oil loving). Conclusions. We proposed a classifier based on LR-XGBoost which used fourteen variables of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of T2DM. The classifier can accurately screen the risk of diabetes in the early phrase, and the degree of variables’ importance scores gives a clue to prevent diabetes occurrence.