ArticleScientific reports2022
Machine learning models for prediction of HF and CKD development in early-stage type 2 diabetes patients.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 4 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 4 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Risk prediction models for detecting a new diagnosis of heart failure within 5 years in the community: a systematic review.BMJ open · 2026Pooled it
- Diagnostic Performance of Machine Learning Algorithms for Predicting Heart Failure in Diabetic Patients: A Systematic Review and Meta-Analysis.Endocrinology, diabetes & metabolism · 2025Pooled it
- Pooled it
- Machine Learning Models for Prediction of Diabetic Microvascular Complications.Journal of diabetes science and technology · 2024Pooled it
- Association of Estimated Pulse Wave Velocity with Chronic Kidney Disease Risk: A Machine Learning Analysis Based on NHANES and CHARLS.Healthcare (Basel, Switzerland) · 2026Article
- Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies.World journal of nephrology · 2026Review
- Development and validation of clinical prediction models for personalized renal function monitoring in people with heart failure in primary care: the RENAL-HF study protocol.European heart journal. Digital health · 2026Article
- Development of a high-altitude renal disease diagnostic model based on machine learning and multiple biomarker detection: a retrospective study of 19,068 patients.BMC nephrology · 2026Article
- Review
- Impact of Residential Social Deprivation on Prediction of Heart Failure in Patients With Type 2 Diabetes: External Validation and Recalibration of the WATCH-DM Score Using Real World Data.Circulation. Cardiovascular quality and outcomes · 2024Article
- Predicting CKD progression using time-series clustering and light gradient boosting machines.Scientific reports · 2024Article
- Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation.Frontiers in endocrinology · 2024Article
- Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations.PloS one · 2023Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic kidney disease (CKD) and heart failure (HF) are the first and most frequent comorbidities associated with mortality risks in early-stage type 2 diabetes mellitus (T2DM). However, efficient screening and risk assessment strategies for identifying T2DM patients at high risk of developing CKD and/or HF (CKD/HF) remains to be established. This study aimed to generate a novel machine learning (ML) model to predict the risk of developing CKD/HF in early-stage T2DM patients. The models were derived from a retrospective cohort of 217,054 T2DM patients without a history of cardiovascular and renal diseases extracted from a Japanese claims database. Among algorithms used for the ML, extreme gradient boosting exhibited the best performance for CKD/HF diagnosis and hospitalization after internal validation and was further validated using another dataset including 16,822 patients. In the external validation, 5-years prediction area under the receiver operating characteristic curves for CKD/HF diagnosis and hospitalization were 0.718 and 0.837, respectively. In Kaplan-Meier curves analysis, patients predicted to be at high risk showed significant increase in CKD/HF diagnosis and hospitalization compared with those at low risk. Thus, the developed model predicted the risk of developing CKD/HF in T2DM patients with reasonable probability in the external validation cohort. Clinical approach identifying T2DM at high risk of developing CKD/HF using ML models may contribute to improved prognosis by promoting early diagnosis and intervention.
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