ArticleScientific reports2023
Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Effect of carnosine or beta-alanine supplementation therapy for prediabetes or type 2 diabetes mellitus: a systematic review and meta-analysis of randomized controlled trials.BMC endocrine disorders · 2025Pooled it
- Structural and functional brain abnormal alteration in patients with type 2 diabetes mellitus: A coordinate-based meta-analysis.Translational psychiatry · 2025Pooled it
- Machine Learning Framework for HbA1c Prediction: Data Enrichment, Cost Optimization, and Interpretability Through Stratified Regression and Multi-Stage Feature Selection.Diagnostics (Basel, Switzerland) · 2026Article
- The prevalence of anemia among type 2 diabetes mellitus with respect to renal function at a tertiary care hospital: A case-control study.Bioinformation · 2026Article
- Toward a Clinically Actionable, Electronic Health Record-Based Machine Learning Model to Forecast 90-Day Change in Hemoglobin AJMIR diabetes · 2025Article
- A Comprehensive Review of Two-Dimensional Speckle-Tracking Echocardiography in Assessing Right and Left Ventricular Function in Diabetic Patients.Clinical cardiology · 2025Review
- Artificial Intelligence as a Tool for Self-Care in Patients with Type 1 and Type 2 Diabetes-An Integrative Literature Review.Healthcare (Basel, Switzerland) · 2025Review
- Applications of AI in Predicting Drug Responses for Type 2 Diabetes.JMIR diabetes · 2025Article
- Ensemble neural network modelling for stratified HbA1c prediction: Integrating past glucose measurement as a predictor of glycaemic control.Bioinformation · 2025Article
- Predicting incident type 2 diabetes in a Japanese cohort: an 8-year analysis of the NAGALA database.Frontiers in endocrinology · 2025Observational
- Article
- Prediction of glycosylated hemoglobin level in patients with cardiovascular diseases and type 2 diabetes mellitus with respect to anti-diabetic medication.Frontiers in endocrinology · 2024Article
- Application of Machine Learning Algorithms to Predict Uncontrolled Diabetes Using the All of Us Research Program Data.Healthcare (Basel, Switzerland) · 2023Article
- Self-Management, Self-Efficacy, and Glycemic Control Among Adults With T2DM in the West Bank: A Descriptive Study.SAGE open nursingArticle
Corrections and comments
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Authors and funding
15 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) are key indicators reflecting blood glucose control in type 2 diabetes mellitus (T2DM) patients. The purpose of this study is to establish a predictive model for blood glucose changes in T2DM patients after 3 months of treatment, achieving personalized treatment.A retrospective study was conducted on type 2 diabetes mellitus real-world medical data from 4 cities in Sichuan Province, China from January 2015 to December 2020. After data preprocessing, data inputting, data sampling, and feature screening, 16 kinds of machine learning methods were used to construct prediction models, and 5 prediction models with the best prediction performance were screened respectively. A total of 100,000 cases were included to establish the FBG model, and 2,169 cases were established to establish the HbA1c model. The best prediction model both of FBG and HbA1c finally obtained are realized by ensemble learning and modified random forest inputting, the AUC values are 0.819 and 0.970, respectively. The most important indicators of the FBG and HbA1c prediction model were FBG and HbA1c. Medication compliance, follow-up outcome, dietary habits, BMI, and waist circumference also had a greater impact on FBG levels. The prediction accuracy of the models of the two blood glucose control indicators is high and has certain clinical applicability.HbA1c and FBG are mutually important predictors, and there is a close relationship between them.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.