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Interpretable machine learning model integrating clinical and elastosonographic features to detect renal fibrosis in Asian patients with chronic kidney disease.

Ziman ChenYingli WangMichael Tin Cheung YingZhongzhen Su
Published in: Journal of nephrology (2024)
This study proposed an XGBoost model for distinguishing moderate-severe renal fibrosis from mild forms in CKD patients, which could be used to assist clinicians in decision-making and follow-up strategies. Moreover, the SHAP algorithm makes it feasible to visualize and interpret the feature processing and diagnostic processes of the model output.
Keyphrases
  • machine learning
  • end stage renal disease
  • chronic kidney disease
  • decision making
  • deep learning
  • newly diagnosed
  • ejection fraction
  • artificial intelligence
  • palliative care
  • high intensity
  • neural network