Improving Chronic Kidney Disease Detection Efficiency: Fine Tuned CatBoost and Nature-Inspired Algorithms with Explainable AI
Journal:
arXiv
Published Date:
Apr 5, 2025
Abstract
Chronic Kidney Disease (CKD) is a major global health issue which is
affecting million people around the world and with increasing rate of
mortality. Mitigation of progression of CKD and better patient outcomes
requires early detection. Nevertheless, limitations lie in traditional
diagnostic methods, especially in resource constrained settings. This study
proposes an advanced machine learning approach to enhance CKD detection by
evaluating four models: Random Forest (RF), Multi-Layer Perceptron (MLP),
Logistic Regression (LR), and a fine-tuned CatBoost algorithm. Specifically,
among these, the fine-tuned CatBoost model demonstrated the best overall
performance having an accuracy of 98.75%, an AUC of 0.9993 and a Kappa score of
97.35% of the studies. The proposed CatBoost model has used a nature inspired
algorithm such as Simulated Annealing to select the most important features,
Cuckoo Search to adjust outliers and grid search to fine tune its settings in
such a way to achieve improved prediction accuracy. Features significance is
explained by SHAP-a well-known XAI technique-for gaining transparency in the
decision-making process of proposed model and bring up trust in diagnostic
systems. Using SHAP, the significant clinical features were identified as
specific gravity, serum creatinine, albumin, hemoglobin, and diabetes mellitus.
The potential of advanced machine learning techniques in CKD detection is shown
in this research, particularly for low income and middle-income healthcare
settings where prompt and correct diagnoses are vital. This study seeks to
provide a highly accurate, interpretable, and efficient diagnostic tool to add
to efforts for early intervention and improved healthcare outcomes for all CKD
patients.
