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The serious condition known as chronic kidney disease (CKD) is characterized by a progressive deterioration of kidney function, which leaves the body unable to adequately eliminate waste products and extra fluid. Chronic renal illness is associated with several effects, some of which are potentially fatal. The effectiveness of several ML algorithms for timely identification of persistent kidney disease was investigated in this study. Specifically, the random forest and gradient boosting classifiers showed remarkable performance, proving their efficacy in CKD prediction. A deeper comprehension of the factors impacting persistent renal disorder was also made possible by the use of the explainers SHAP and LIME.

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Deciphering Chronic Kidney Disease Diagnosis: A Comparative Exploration of Computational Approaches with LIME and SHAP Interpretation

  • 会议论文

  • 发表日期：

    2026年

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  • J. P. Harshini,
  • K. Raksha,
  • Ashwini Kodipalli,
  • Trupthi Rao,
  • Jami Venkata Suman

摘要

The serious condition known as chronic kidney disease (CKD) is characterized by a progressive deterioration of kidney function, which leaves the body unable to adequately eliminate waste products and extra fluid. Chronic renal illness is associated with several effects, some of which are potentially fatal. The effectiveness of several ML algorithms for timely identification of persistent kidney disease was investigated in this study. Specifically, the random forest and gradient boosting classifiers showed remarkable performance, proving their efficacy in CKD prediction. A deeper comprehension of the factors impacting persistent renal disorder was also made possible by the use of the explainers SHAP and LIME.