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

Abstract

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.

Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques

“…It was a good example of success for the XGBoost’s application in the research of diabetes risk prediction. This finding was consistent with earlier studies [ 14 , 21 , 27 , 53 ], which identified the good prediction power of the XGBoost model, with AUC values ranging from 0.8300 to 0.9680. Different from this study, a previous Korean population-based cohort study demonstrated that the ensemble models (e.g., stacking classifier) had better performance than the single models including XGBoost [ 54 ].…”

“…A potential reason could be due to the differences of the study population and input features in the models, which could impact the predictive performance to some extent. Different from our study, the study population of prior studies [ 14 , 21 , 27 , 53 ] were middle-aged adults and fewer predictors were applied in the prediction of diabetes. To our knowledge, this was the first study that targeted the elderly population (≥65 years) in China to build predictive models for diabetes using machine learning techniques, which would have great implications for designing diabetes prevention focusing on the elderly.…”

“…A study in Luzhou, China utilized four ML algorithms to build prediction models of diabetes mellitus by using hospital physical examination data, and it revealed that random forest was the best performing model with the highest accuracy of 0.8084 [ 26 ]. A cross-sectional study in Urumqi, China based on the national physical examination data reported that XGBoost was the best classifier with AUC of 0.9680 [ 27 ]. Obviously, prior studies demonstrated different results in T2DM prediction even using the same machine learning algorithms [ 28 ].…”

Predicting the 2-Year Risk of Progression from Prediabetes to Diabetes Using Machine Learning among Chinese Elderly Adults

“…In both forecast periods, the XGBoost model performed relatively well. This was not unexpected; the predictive ability of XGBoost has manifested in previous studies of diabetes onset [17] and complications [18]. As an ensemble machine learning algorithm, XGBoost was not affected by the correlation of independent variables, which was exactly the problem that the LR model needed to solve.…”

A Noninvasive Prediction Model for Hepatitis B Virus Disease in Patients with HIV: Based on the Population of Jiangsu, China

“…It is difficult to use traditional logistic regression to process demographic and serological data which are often nonlinear, abnormal, and heterogeneous [ 9 ]. But machining learning (ML) provides a chance.…”