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September 5, 2025Open Access

Early prediction of severity progression in patients with chronic kidney disease: A Machine Learning Predictive Modelling analysis with retrospective data of a tertiary care hospital

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Authors

SNSaurav NayakGSGautom Kumar SahariaSPSandip Kumar Panda

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Overview

Retrospective analysis reveals serum C3 and C4 as biomarkers in predicting chronic kidney disease severity, suggesting better management strategies.

Key Points

  • Serum complements C3 and C4 can predict chronic kidney disease severity effectively.
  • The Random Forest model achieved an outstanding F1 score of 0.984 and accuracy of 0.991.
  • Influential predictors included age, serum C3 levels, and the C3/C4 ratio in the predictive modeling.
  • A web-based tool was developed to aid clinicians in estimating chronic kidney disease severity based on key markers.

Cite This Study

Nayak et al. (2025) studied this question.

synapsesocial.com/papers/68c239e5b210217d6477e755https://doi.org/10.21203/rs.3.rs-7198292/v1
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