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@article{175682,
        author = {Rangaraj L and Anandhakumar S and Rohith K and Dr. V. Manoranjithem},
        title = {Interpretable Machine Learning for Early Detection of Chronic Kidney Disease Using XAI Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3517-3522},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175682},
        abstract = {chronic kidney disease (CKD) is often diagnosed at later stages, leading to severe health impacts. This study presents a machine learning-based approach for early CKD prediction using patient clinical data. To improve model transparency, Explainable AI (XAI) techniques like LIME are employed, offering insights into feature contributions. The proposed system achieves high accuracy and supports clinical decision-making by identifying key indicators influencing CKD onset.},
        keywords = {chronic kidney disease (CKD), Explainable Artificial Intelligence (XAI), LIME, Supervised Learning, Early Diagnosis, Healthcare Analytics, Classification, Clinical Data, Machine Learning, Interpretability.},
        month = {April},
        }