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@article{198137,
        author = {Ambar Dutta and Saraswan Chakraborty},
        title = {AI-Driven Early Detection of Chronic Kidney Disease: A Predictive Modelling Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9441-9447},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198137},
        abstract = {Chronic Kidney Disease (CKD) is a globally prevalent, progressive medical condition that leads to irreversible deterioration of renal function and, in advanced stages, end-stage renal disease (ESRD). Early and accurate detection of CKD remains a critical clinical challenge, as the disease is largely asymptomatic in its early stages. Artificial Intelligence (AI) and Machine Learning (ML) methodologies have emerged as powerful tools for clinical decision support, offering the potential to detect CKD with higher accuracy and at earlier stages than traditional diagnostic methods. This paper presents a comprehensive review and comparative analysis of AI-based algorithms — including Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Gradient Boosting (XGBoost, LightGBM), and Deep Learning architectures — applied to CKD prediction using publicly available and clinical datasets. Our study evaluates each algorithm across key performance metrics such as accuracy, sensitivity, specificity, F1-score, and AUC-ROC. Experimental results on the UCI CKD dataset demonstrate that the ensemble Random Forest model achieves the highest overall accuracy of 98.7%, while deep learning models exhibit superior generalization on larger heterogeneous datasets. This review further identifies research gaps including the need for multi-modal data integration, explainability in black-box models, and real-time deployment in resource-constrained clinical settings.},
        keywords = {Chronic Kidney Disease, Machine Learning, Artificial Intelligence, Random Forest, Support Vector Machine, Deep Learning, Clinical Decision Support, Early Detection, Renal Function, Predictive Modeling.},
        month = {April},
        }