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

Mingyue Xue,Yinxia Su,Shuxia Wang,Chen Li,Hua Yao

doi:10.1155/2020/6873891

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.

Highlights

Diabetes, as a group of metabolic disorders, is characterized by hyperglycemia, which can lead to many serious conditions such as heart disease, kidney disease, vision loss, and lower limb amputation [1]
Each subject was composed of 18 variables (Table 1), including age, body mass index (BMI), gender, waist circumference, ethnicity, drinking, physical activity, smoking, eating habits, blood pressure, fatty liver, and hypertension
It was shown that age, BMI, waist circumference, systolic pressure, ethnicity, physical activity, drinking status, weekly drinking amount (g), daily smoking amount, smoking status, dietary ratio, diet habit, fatty liver, and hypertension are statistically significant factors for T2DM at a 5% level of significance and the rest of the factors are insignificant. These 14 variables were used for tree-based machine learning (ML) algorithms to classify T2DM

Summary

Introduction

As a group of metabolic disorders, is characterized by hyperglycemia, which can lead to many serious conditions such as heart disease, kidney disease, vision loss, and lower limb amputation [1]. 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. 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

Methods

Results

Discussion

Conclusion