Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes
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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.
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In Fan et al. (2021), XF was the best performing model for predicting diabetic nephropathy and diabetic angiopathy, D for diabetic neuropathy and diabetic eye disease, and BN for HbA1c.
“XF was the best performing model for predicting diabetic nephropathy and diabetic angiopathy, D for predicting diabetic neuropathy and diabetic eye disease and BN for HbA1c (Fan et al., 2021).”
From Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature (2022) · cites this paper as Fan et al. (2021)
Fan et al. (2021) reported AUC of 0.847 ± 0.081 for XF, 0.787 ± 0.081 for CHAID, and 0.720 ± 0.060 for QUEST.
“Fan et al. (2021) reported AUC of 0.847 ± 0.081 for XF, 0.787 ± 0.081 for CHAID and 0.720 ± 0.060 for QUEST.”
From Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature (2022) · cites this paper as Fan et al. (2021)
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- Authors
- Fan, Y., Long, E., Cai, L., Cao, Q., Wu, X., & Tong, R.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Sichuan University (CN)
- Corresponding author
- Wu, Xingwei
- Tong, Rongsheng
- Venue
- Frontiers in Pharmacology
- Publisher
- Frontiers Media SA
- Published
- 2021-06-22
- Volume
- 12
- Pages
- 665951-665951
- ISSN
- 1663-9812 (ISSN-L)
- PMC
- PMC8258097
- OpenAlex
- W3173214468
- Cited by
- 58
- References
- 52
- Citations by year
- 2026: 9 · 2025: 17 · 2024: 13 · 2023: 13 · 2022: 5 · 2021: 1
- FWCI
- 5.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Pharmacology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Department of Science and Technology of Sichuan Province (2019YFS0514)
- Health Commission of Sichuan Province (2019YFS0514)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
- Keywords
- type 2 diabetes, medication non-adherence, diabetes complications, glycemic control, HbA1c, diabetic nephropathy, diabetic peripheral neuropathy, diabetic vascular disease, diabetic eye disease, risk prediction model, antidiabetic drugs, diabetes duration
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