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Estimation of Diabetes in a High-Risk Adult Chinese Population Using J48 Decision Tree Model
Dongmei Pei1, Tengfei Yang1, Chengpu Zhang1
1Department of Health Management, Shengjing Hospital of China Medical University, Shenyang, People's Republic of China.
Diabetes, Metabolic Syndrome and Obesity : Targets and Therapy
|December 4, 2020
Summary
A new decision tree model accurately predicts diabetes risk in high-risk adults. This cost-effective approach identifies modifiable factors, aiding early diabetes management and diagnosis.
Area of Science:
- Computational biology
- Medical informatics
- Public health
Background:
- Diabetes mellitus is a global health concern requiring early detection.
- Conventional screening methods pose challenges due to cost and potential side effects.
- Developing accessible predictive models is crucial for at-risk populations.
Purpose of the Study:
- To develop a simple, reliable predictive model for diabetes.
- To utilize a decision tree algorithm based on associated risk factors.
- To enable early diagnosis and management of diabetes.
Main Methods:
- A retrospective cross-sectional study involving 10,436 participants.
- Data mining techniques refined the dataset to 3454 participants.
- A cost-sensitive J48 decision tree algorithm was employed for model development.
Main Results:
- The decision tree model identified key, modifiable risk factors for diabetes.
- Achieved a classification accuracy of 90.3%.
- Demonstrated high precision (89.7%) and recall (90.3%).
Conclusions:
- The decision tree model offers a cost-effective method for diabetes risk prediction.
- The model shows strong potential for implementation in diabetes management programs.
- Facilitates early identification of diabetes in high-risk Chinese adults.
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