ArticleScientific reports2022
Machine learning to predict end stage kidney disease in chronic kidney disease.
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 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it, 147 citations in OpenAlex.
- Representation of multimorbidity and frailty in the development and validation of kidney failure prognostic prediction models: a systematic review.BMC medicine · 2024Pooled it
- Sexual dysfunction in peritoneal dialysis: associated factors and exploratory machine-learning classification.International urology and nephrology · 2026Article
- Development and external validation of an explainable machine learning model for predicting chronic kidney disease progression in the Korean population.Kidney research and clinical practice · 2026Article
- An explainable machine learning model for predicting high phosphorus risk in patients on maintenance hemodialysis: a multicenter retrospective study.BMC medical informatics and decision making · 2026Article
- Toward Explainable Precision Nephrology: Machine Learning-Based Chronic Kidney Disease Prediction.Biomedicines · 2026Article
- Predicting End-Stage Renal Disease and Mortality in Chronic Kidney Disease Using Machine Learning: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Emerging Urinary Biomarkers and Innovative Technologies for the Early Detection and Personalized Management of Chronic Kidney Disease.International journal of molecular sciences · 2026Review
- 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
- The Association of Social Determinants of Health on Mortality among Adults with End-Stage Kidney Disease: a Systematic Review and Meta-Analysis.Journal of racial and ethnic health disparities · 2026Review
- Risk Prediction of Chronic Kidney Disease Progression in Type 2 Diabetes Mellitus Across Diverse Populations.NPJ digital medicine · 2026Article
- Prevalence, severity and multidimensional factors associated with pain among hemodialysis patients: A cross-sectional study in a resource-limited healthcare system.SAGE open medicine · 2026Article
- A machine learning-driven early warning system for cryptocaryoniasis in marine aquaculture.Parasites & vectors · 2025Article
- Stacking ensemble model for predicting chronic kidney disease in the Uddanam region of India with unknown etiology.Scientific reports · 2025Article
- Machine learning models for predicting short-term progression in patients with stage 4 chronic kidney disease: a multi-center validation study.Scientific reports · 2025Observational
- Survival prediction models for people living with HIV based on four machine learning models.Scientific reports · 2025Article
- Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques.BMC medical informatics and decision making · 2025Article
- Ensemble machine learning for predicting renal function decline in chronic kidney disease: development and external validation.Frontiers in medicine · 2025Article
- Noninvasive prediction of T-score in IgA nephropathy using machine learning within the Oxford classification system.Frontiers in endocrinology · 2025Article
- Exploiting biochemical data to improve osteosarcoma diagnosis with deep learning.Health information science and systems · 2024Article
- Machine Learning-Based Prediction for Incident Hypertension Based on Regular Health Checkup Data: Derivation and Validation in 2 Independent Nationwide Cohorts in South Korea and Japan.Journal of medical Internet research · 2024Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study was to assess the feasibility of machine learning (ML) in predicting the risk of end-stage kidney disease (ESKD) from patients with chronic kidney disease (CKD). Data were obtained from a longitudinal CKD cohort. Predictor variables included patients' baseline characteristics and routine blood test results. The outcome of interest was the presence or absence of ESKD by the end of 5 years. Missing data were imputed using multiple imputation. Five ML algorithms, including logistic regression, naïve Bayes, random forest, decision tree, and K-nearest neighbors were trained and tested using fivefold cross-validation. The performance of each model was compared to that of the Kidney Failure Risk Equation (KFRE). The dataset contained 748 CKD patients recruited between April 2006 and March 2008, with the follow-up time of 6.3 ± 2.3 years. ESKD was observed in 70 patients (9.4%). Three ML models, including the logistic regression, naïve Bayes and random forest, showed equivalent predictability and greater sensitivity compared to the KFRE. The KFRE had the highest accuracy, specificity, and precision. This study showed the feasibility of ML in evaluating the prognosis of CKD based on easily accessible features. Three ML models with adequate performance and sensitivity scores suggest a potential use for patient screenings. Future studies include external validation and improving the models with additional predictor variables.
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