Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 3
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Predictive Modelling of Kidney Failure Using Advanced Machine Learning Techniques: A Comprehensive Review

Author(s) Aditya Padmakar Pethkar, Prof. Malvika Saraf, Sonal Daburkar, Umesh Bhomale
Country India
Abstract Chronic Kidney Disease (CKD) is a growing global health concern characterized by progressive loss of kidney functionand late-stage diagnosis due to its asymptomatic early progression. Recent advancements in artificial intelligence (AI) andmachine learning (ML) have introduced data-driven approaches capable of improving early detection, risk prediction, anddisease management. This review paper examines current research trends in CKD prediction using advanced machinelearning techniques, including traditional algorithms, ensemble models, deep learning, and multimodal data integration. Thestudy 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 highpredictive accuracy, while deep learning approaches show promise in imaging-based diagnosis. However, challenges such aslimited dataset diversity, lack of external validation, model interpretability issues, and ethical concerns remain barriers toclinical adoption. The review highlights the importance of explainable AI, standardized datasets, and multimodal integrationto enhance reliability. Overall, AI-driven CKD prediction systems have strong potential to support clinical decision-makingand enable early intervention strategies.
Keywords Chronic Kidney Disease, Machine Learning, Early Diagnosis, Predictive Modeling, Healthcare Analytics etc.
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-08
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E-ISSN 3048-7641
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