ArticleScientific reports2025
Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3-5 and end-stage kidney disease.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Applications of Machine Learning for Early Diagnosis and Prognosis of Chronic Kidney Disease: Current Evidence.Diagnostics (Basel, Switzerland) · 2026Review
- An explainable machine learning model for predicting one-year osteoporosis risk: development and validation in a prospective cohort.BMC musculoskeletal disorders · 2026Article
- Predicting Chronic Kidney Disease from Biomarkers: An Explainable Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Prediction of Imminent Peritoneal Dialysis-Associated Peritonitis Using Time-Updated Electronic Health Records and Machine Learning: A Temporal Validation Study.Journal of inflammation research · 2026Article
- Biomarkers for Chronic Kidney Disease: An Updated Review of Innovative Detection Approaches.Current aging science · 2026Review
- Enhanced early chronic kidney disease prediction using hybrid waterwheel plant algorithm for deep neural network optimization.Scientific reports · 2025Article
- Stacking ensemble model for predicting chronic kidney disease in the Uddanam region of India with unknown etiology.Scientific reports · 2025Article
- Early detection of chronic kidney disease using deep learning: a Mini review.Frontiers in digital health · 2025Review
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Authors and funding
6 authors.
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Abstract
Chronic kidney disease-mineral bone disorder is a common complication in patients with chronic kidney disease (CKD) and end-stage kidney disease (ESKD), and it increases the risk of osteoporosis and fractures. This study aimed to develop predictive machine-learning (ML) models to identify osteoporosis risk in patients with CKD stages 3-5 and ESKD. We retrospectively analyzed a de-identified osteoporosis database from a Taiwanese hospital, including 6614 patients with CKD stages 3-5 and ESKD who underwent bone mineral density (BMD) scans between January 2011 and June 2022. Nine ML algorithms were applied to predict osteoporosis: logistic regression, XGBoost, LightGBM, CatBoost, SVM, decision tree, random forest, k-nearest neighbors, and an artificial neural network (ANN). The ANN model achieved the highest predictive performance, with an area under the curve (AUC) of 0.940 on the validation and 0.930 on the test datasets. The receiver operating characteristic curve, confusion matrix, and predictive probability histogram revealed that the ANN model performed well in terms of discrimination. Calibration and decision curve analyses further demonstrated the reliability and applicability of the ANN model. The ANN model demonstrated the potential for clinical implementation in screening high-risk patients for osteoporosis.
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