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Synapse
July 29, 2026DiagnosticsOpen Access

Applications of Machine Learning for Early Diagnosis and Prognosis of Chronic Kidney Disease: Current Evidence

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Authors

LPLeon Van de PutteMSMarijn M. Speeckaert

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Overview

Review examines machine learning methods for diagnosing and predicting chronic kidney disease, suggesting a need for robust validation.

Key Points

  • This review evaluates the use of machine learning for early diagnosis and prognosis of chronic kidney disease (CKD).
  • Reviewed literature from 2016 to 2025 in online databases: PubMed, Web of Science, Embase.
  • Screened 57 articles focusing on machine learning applications in CKD diagnosis and progression.
  • Analysis of diverse data types including clinical, laboratory, imaging, and at-home measurements.
  • High discriminatory performance reported in many studies, though citing high risk of bias.
  • Dominance of retrospective, single-center studies with methodological variability.
  • Need for prospective multicenter studies for clinical validation and improvement over existing care standards.

Cite This Study

Putte et al. (2026) studied this question.

synapsesocial.com/papers/6a69a27dc8da07d9defa5f30https://doi.org/10.3390/diagnostics16152354
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Clinical Application of Machine Learning Models for Early-Stage Chronic Kidney Disease Detection2025 · 25 citations
  2. 2Machine Learning and Chronic Kidney Disease: Towards Early Prediction and Diagnosis2024
  3. 3Machine Learning and Chronic Kidney Disease: Towards Early Prediction and Diagnosis2024
  4. 4Early Prediction and Progrssion of Chronic Kidney Disease Using Machine Lerning Techniques2024 · 12 citations
  5. 5Chronic Kidney Disease Prediction using Machine Learning2024 · 1 citations