Predictive Modelling of Kidney Failure using Advanced Machine Learning Techniques: A Comprehensive Review

Chronic Kidney Disease (CKD) is a growing global health concern characterized by progressive loss of kidney function and late-stage diagnosis due to its asymptomatic early progression. Recent advancements in artificial intelligence (AI) and machine learning (ML) have introduced data-driven approaches capable of improving early detection, risk prediction, and disease management. This review paper examines current research trends in CKD prediction using advanced machine learning techniques, including traditional algorithms, ensemble models, deep learning, and multimodal data integration. The study evaluates data preprocessing strategies, feature selection methods, and model evaluation techniques used in existing literature. Findings indicate that ensemble methods such as Random Forest and Gradient Boosting consistently achieve high predictive accuracy, while deep learning approaches show promise in imaging-based diagnosis. However, challenges such as limited dataset diversity, lack of external validation, model interpretability issues, and ethical concerns remain barriers to clinical adoption. The review highlights the importance of explainable AI, standardized datasets, and multimodal integration to enhance reliability. Overall, AI-driven CKD prediction systems have strong potential to support clinical decision-making and enable early intervention strategies.