Estimation of Diabetes in a High-Risk Adult Chinese Population Using J48 Decision Tree Model

Abstract

Background

To predict and make an early diagnosis of diabetes is a critical approach in a population with high risk of diabetes, one of the devastating diseases globally. Traditional and conventional blood tests are recommended for screening the suspected patients; however, applying these tests could have health side effects and expensive cost. The goal of this study was to establish a simple and reliable predictive model based on the risk factors associated with diabetes using a decision tree algorithm.

Methods

A retrospective cross-sectional study was used in this study. A total of 10,436 participants who had a health check-up from January 2017 to July 2017 were recruited. With appropriate data mining approaches, 3454 participants remained in the final dataset for further analysis. Seventy percent of these participants (2420 cases) were then randomly allocated to either the training dataset for the construction of the decision tree or the testing dataset (30%, 1034 cases) for evaluation of the performance of the decision tree. For this purpose, the cost-sensitive J48 algorithm was used to develop the decision tree model.

Results

Utilizing all the key features of the dataset consisting of 14 input variables and two output variables, the constructed decision tree model identified several key factors that are closely linked to the development of diabetes and are also modifiable. Furthermore, our model achieved an accuracy of classification of 90.3% with a precision of 89.7% and a recall of 90.3%.

Conclusion

By applying simple and cost-effective classification rules, our decision tree model estimates the development of diabetes in a high-risk adult Chinese population with strong potential for implementation of diabetes management.

Keywords:

• diabetes
• J48 algorithm
• decision tree
• risk factors

Abbreviations

BMI, body mass index; WHO, World Health Organization; SPSS, Statistical Package for Social Sciences; WEKA, Waikato Environment for Knowledge Analysis; T2DM, type 2 diabetes mellitus.

Data Sharing Statement

The datasets used and analyzed in the present study could be available from the corresponding author upon a reasonable request.

Ethics Approval and Consent to Participate

This human study was approved by Shengjing Hospital of China Medical University Ethics Committee (ref. Ethics 2017PS42K). All participants in this study received informed consent and agreed to participate in the study. This study complied with the Declaration of Helsinki.

Acknowledgments

We are grateful to Lincoln C. Chen, president of China Medical Board, for supporting this work and researchers.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

All authors declare that they have no conflicts of interests.

Additional information

Funding

This study was funded by China Medical Board under the grant number #15-219.