Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach

الملخص EN

Background.

Diabetes mellitus is a chronic disease with a steadfast increase in prevalence.

Due to the chronic course of the disease combining with devastating complications, this disorder could easily carry a financial burden.

The early diagnosis of diabetes remains as one of the major challenges medical providers are facing, and the satisfactory screening tools or methods are still required, especially a population- or community-based tool.

Methods.

This is a retrospective cross-sectional study involving 15,323 subjects who underwent the annual check-up in the Department of Family Medicine of Shengjing Hospital of China Medical University from January 2017 to June 2017.

With a strict data filtration, 10,436 records from the eligible participants were utilized to develop a prediction model using the J48 decision tree algorithm.

Nine variables, including age, gender, body mass index (BMI), hypertension, history of cardiovascular disease or stroke, family history of diabetes, physical activity, work-related stress, and salty food preference, were considered.

Results.

The accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC) value for identifying potential diabetes were 94.2%, 94.0%, 94.2%, and 94.8%, respectively.

The structure of the decision tree shows that age is the most significant feature.

The decision tree demonstrated that among those participants with age≤49, 5497 participants (97%) of the individuals were identified as nondiabetic, while age>49, 771 participants (50%) of the individuals were identified as nondiabetic.

In the subgroup where people were 34

Work-related stress was identified as being associated with diabetes.

In individuals with 34

Conclusions.

We proposed a classifier based on a decision tree which used nine features of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of diabetes.

The classifier indicates that a decision tree analysis can be successfully applied to screen diabetes, which will support clinical practitioners for rapid diabetes identification.

The model provides a means to target the prevention of diabetes which could reduce the burden on the health system through effective case management.