Improving Chronic Kidney Disease Detection Efficiency: Fine Tuned CatBoost and Nature-Inspired Algorithms with Explainable AI

2025-04-05

AI Summary

To address the low early detection efficiency and poor model interpretability of chronic kidney disease (CKD) in resource-constrained settings, this study proposes an optimized CatBoost framework integrating simulated annealing (for feature selection), cuckoo search (for outlier correction), and SHAP-based interpretability analysis. The framework simultaneously enhances model robustness and clinical credibility without compromising predictive accuracy. Evaluated on a public CKD dataset, it achieves 98.75% accuracy, an AUC of 0.9993, and a Cohen’s kappa of 0.9735. SHAP analysis identifies urine specific gravity, serum creatinine, and albumin as the top three predictive biomarkers. To the best of our knowledge, this is the first work to synergistically embed three distinct metaheuristic optimization algorithms with explainable AI within a gradient-boosting framework. The resulting solution offers high performance, clinical verifiability, and deployment feasibility for primary-care CKD screening.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Enhancing CKD detection using advanced machine learning models
• Improving diagnostic accuracy with nature-inspired algorithms and XAI
• Addressing resource constraints in low-income healthcare settings

Innovation

• Fine-tuned CatBoost with nature-inspired algorithms
• Feature selection using Simulated Annealing
• Explainable AI via SHAP for transparency