Kidney Disease Detection Using Machine Learning with Explainable Artificial Intelligence

This research provides a comprehensive systematic review of machine learning-based approaches for Chronic Kidney Disease (CKD) detection with a focus on Explainable Artificial Intelligence (XAI). The study identifies the effectiveness of widely used ML models, including Random Forest and XGBoost, and analyzes how explanation techniques such as SHAP and LIME improve model transparency. Additionally, it highlights key research challenges, including limited dataset diversity, insufficient clinical validation, and the need for interpretable AI solutions in healthcare. The findings provide valuable insights for developing reliable, transparent, and clinically applicable CKD detection systems.