Using Machine Learning for the Risk Factors Classification of Glycemic Control in Type 2 Diabetes Mellitus
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participants
2.2. Measurement
2.3. Data Reduction and Statistical Analysis
3. Results
3.1. Regression Tree
3.2. Comparisons of AI Machine Learning Classification Methods
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | Testing Data | |||
---|---|---|---|---|
% | Sensitivity | Specificity | Area under Curves | Accuracy Rate |
Boosting | 51% | 63% | 63% | 57% |
Support vector machine | 52% | 65% | 58% | 58% |
Classification tree | 59% | 80% | 69% | 69% |
Neural network | 69% | 80% | 60% | 74% |
K-nearest neighbors | 63% | 93% | 78% | 78% |
Random forest | 77% | 91% | 95% | 84% |
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Cheng, Y.-L.; Wu, Y.-R.; Lin, K.-D.; Lin, C.-H.R.; Lin, I.-M. Using Machine Learning for the Risk Factors Classification of Glycemic Control in Type 2 Diabetes Mellitus. Healthcare 2023, 11, 1141. https://doi.org/10.3390/healthcare11081141
Cheng Y-L, Wu Y-R, Lin K-D, Lin C-HR, Lin I-M. Using Machine Learning for the Risk Factors Classification of Glycemic Control in Type 2 Diabetes Mellitus. Healthcare. 2023; 11(8):1141. https://doi.org/10.3390/healthcare11081141
Chicago/Turabian StyleCheng, Yi-Ling, Ying-Ru Wu, Kun-Der Lin, Chun-Hung Richard Lin, and I-Mei Lin. 2023. "Using Machine Learning for the Risk Factors Classification of Glycemic Control in Type 2 Diabetes Mellitus" Healthcare 11, no. 8: 1141. https://doi.org/10.3390/healthcare11081141
APA StyleCheng, Y.-L., Wu, Y.-R., Lin, K.-D., Lin, C.-H. R., & Lin, I.-M. (2023). Using Machine Learning for the Risk Factors Classification of Glycemic Control in Type 2 Diabetes Mellitus. Healthcare, 11(8), 1141. https://doi.org/10.3390/healthcare11081141