Abstract
Purpose: The objective of this study was to evaluate the efficacy of machine learning algorithms in predicting risks of complications and poor glycemic control in nonadherent type 2 diabetes (T2D). Materials and Methods: This study was a real-world study of the complications and blood glucose prognosis of nonadherent T2D patients. Data of inpatients in Sichuan Provincial People’s Hospital from January 2010 to December 2015 were collected. The T2D patients who had neither been monitored for glycosylated hemoglobin A nor had changed their hyperglycemia treatment regimens within the last 12 months were the object of this study. Seven types of machine learning algorithms were used to develop 18 prediction models. The predictive performance was mainly assessed using the area under the curve of the testing set. Results: Of 800 T2D patients, 165 (20.6%) met the inclusion criteria, of which 129 (78.2%) had poor glycemic control (defined as glycosylated hemoglobin A ≥7%). The highest area under the curves of the testing set for diabetic nephropathy, diabetic peripheral neuropathy, diabetic angiopathy, diabetic eye disease, and glycosylated hemoglobin A were 0.902 ± 0.040, 0.859 ± 0.050, 0.889 ± 0.059, 0.832 ± 0.086, and 0.825 ± 0.092, respectively. Conclusion: Both univariate analysis and machine learning methods reached the same conclusion. The duration of T2D and the duration of unadjusted hypoglycemic treatment were the key risk factors of diabetic complications, and the number of hypoglycemic drugs was the key risk factor of glycemic control of nonadherent T2D. This was the first study to use machine learning algorithms to explore the potential adverse outcomes of nonadherent T2D. The performances of the final prediction models we developed were acceptable; our prediction performances outperformed most other previous studies in most evaluation measures. Those models have potential clinical applicability in improving T2D care.
Highlights
Diabetes mellitus, characterized by persistent hyperglycemia (Li et al, 2020), is a common chronic disease
The objective of this work was to develop and evaluate prediction models of diabetic complications and poor glycemic control (defined as hemoglobin A1c (HbA1c) ≥7%) among nonadherent Type 2 diabetes (T2D) patients based on ML algorithms and to identify the predictors of complications and HbA1c
Patients with T2D [the World Health Organization (WHO) (1999) criteria were adopted for diagnosis of T2D] were included and would be excluded when he or she visited a medical institution within 12 months, had adjusted their treatment plan within 12 months, did not use chemicals for hypoglycemic therapy, had used traditional Chinese medicine, Chinese herbal medicine, and acupuncture to control glycemia within the last 12 months, and had liver and kidney dysfunction
Summary
Diabetes mellitus, characterized by persistent hyperglycemia (Li et al, 2020), is a common chronic disease. T2D and its complications harshly impact the life quality and the finances of individuals and bring about a heavy economic burden on the national health-care system (Hur et al, 2013; World Health Organization, 2016; Bui et al, 2019; Harding et al, 2019). The prevalence of these complications is generally proportional to the degree of glycemic control and the duration of diabetes (Kidanie et al, 2018). It was necessary to establish a prediction model that could predict the prognosis of nonadherent T2D
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