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- Title
Using Machine Learning for the Risk Factors Classification of Glycemic Control in Type 2 Diabetes Mellitus.
- Authors
Cheng, Yi-Ling; Wu, Ying-Ru; Lin, Kun-Der; Lin, Chun-Hung Richard; Lin, I-Mei
- Abstract
Several risk factors are related to glycemic control in patients with type 2 diabetes mellitus (T2DM), including demographics, medical conditions, negative emotions, lipid profiles, and heart rate variability (HRV; to present cardiac autonomic activity). The interactions between these risk factors remain unclear. This study aimed to use machine learning methods of artificial intelligence to explore the relationships between various risk factors and glycemic control in T2DM patients. The study utilized a database from Lin et al. (2022) that included 647 T2DM patients. Regression tree analysis was conducted to identify the interactions among risk factors that contribute to glycated hemoglobin (HbA1c) values, and various machine learning methods were compared for their accuracy in classifying T2DM patients. The results of the regression tree analysis revealed that high depression scores may be a risk factor in one subgroup but not in others. When comparing different machine learning classification methods, the random forest algorithm emerged as the best-performing method with a small set of features. Specifically, the random forest algorithm achieved 84% accuracy, 95% area under the curve (AUC), 77% sensitivity, and 91% specificity. Using machine learning methods can provide significant value in accurately classifying patients with T2DM when considering depression as a risk factor.
- Subjects
GLYCEMIC control; MACHINE learning; ARTIFICIAL intelligence; TYPE 2 diabetes; DESCRIPTIVE statistics; MENTAL depression; RESEARCH funding; EMOTIONS; SENSITIVITY &; specificity (Statistics); BODY mass index
- Publication
Healthcare (2227-9032), 2023, Vol 11, Issue 8, p1141
- ISSN
2227-9032
- Publication type
Article
- DOI
10.3390/healthcare11081141