Resampling classification model for predicting blood glucose control in middle-aged and elderly diabetic patients in China

WANG Ping; ZHANG Le; HONG Xiaorui; ZHU Suling; ZHAO Xuejing

doi:10.16462/j.cnki.zhjbkz.2024.09.003

Volume 28 Issue 9

Sep. 2024

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WANG Ping, ZHANG Le, HONG Xiaorui, ZHU Suling, ZHAO Xuejing. Resampling classification model for predicting blood glucose control in middle-aged and elderly diabetic patients in China[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(9): 1005-1009. doi: 10.16462/j.cnki.zhjbkz.2024.09.003

Citation:

WANG Ping, ZHANG Le, HONG Xiaorui, ZHU Suling, ZHAO Xuejing. Resampling classification model for predicting blood glucose control in middle-aged and elderly diabetic patients in China[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(9): 1005-1009. doi: 10.16462/j.cnki.zhjbkz.2024.09.003

Citation:

WANG Ping, ZHANG Le, HONG Xiaorui, ZHU Suling, ZHAO Xuejing. Resampling classification model for predicting blood glucose control in middle-aged and elderly diabetic patients in China[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(9): 1005-1009. doi: 10.16462/j.cnki.zhjbkz.2024.09.003

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Resampling classification model for predicting blood glucose control in middle-aged and elderly diabetic patients in China

doi: 10.16462/j.cnki.zhjbkz.2024.09.003

1.
Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou 730000, China
2.
Institute of Probability and Statistics, School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China

WANG Ping and ZHANG Le contributed equally to this article

Funds:

National Natural Science Foundation of China 11971214

More Information

Corresponding author: ZHU Suling, E-mail: zhusl@lzu.edu.cn
Received Date: 2023-11-07
Rev Recd Date: 2024-05-15

Available Online: 2024-10-24

Publish Date: 2024-09-10

Abstract

Abstract

Objective This study aims to improve the prediction performance of blood glucose control classification models for diabetic patients by employing resampling algorithms. Methods Blood glucose control data of diabetic patients in the China health and retirement longitudinal study (CHARLS) database were resampled. We compared the classification performance of logistic regression (LR), support vector machines (SVM), and random forests (RF) before and after resampling. We utilized stratified 5-fold cross-validation and area under curve (AUC) to determine the optimal parameters of the models. The performance of the classification models before and after resampling was evaluted using metrics such as accuracy, sensitivity, specificity, precision, geometric mean (G-mean), F1 score, and AUC. Results All three resampling algorithms, including ADASYN, synthetic minority over-sampling technique and edited nearest neighbors (SMOTE-ENN), and synthetic minority over-sampling technique tomek (SMOTE-Tomek), enhanced the prediction performance of three classification models when dealing with imbalanced blood glucose control data in diabetic patients. These algorithms exhibited varying degrees of improvement in AUC values, with adaptive synthetic sampling (ADASYN) increasing the AUC value of the logistic classification model by 2.13%, SMOTE-ENN by 3.05%, and SMOTE-Tomek by 2.13%, respectively. Conclusions ADASYN, SMOTE-ENN, and SMOTE-Tomek can better deal with the imbalanced blood glucose control data in diabetic patients and improve the performance of blood glucose control classification models.
- Resampling algorithm,
- Imbalance classification,
- Diabetes,
- Blood glucose control

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References(10)

References

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