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@article{188765,
        author = {R.ROHINI and K.Dharani and P.Yogasree},
        title = {predictive machine learing approaches for chronic kindey disease},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {3162-3167},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188765},
        abstract = {chronic kidney disease (CKD) is a progressive medical condition that leads to a gradual loss of kidney function over time and has become a global health concern due to its increasing prevalence and associated healthcare burden. Early detection of CKD is essential to prevent severe complications such as kidney failure and cardiovascular diseases. Traditional diagnostic methods rely heavily on clinical tests and physician interpretation, which can be time-consuming and prone to human error. In recent years, predictive machine learning (ML) approaches have shown promising results in improving the accuracy, efficiency, and timeliness of CKD diagnosis. This study presents a comprehensive review of various machine learning algorithms—including Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbors, and Deep Learning models—used for CKD prediction. The paper highlights key datasets, feature selection methods, model evaluation metrics, and comparative performances of these algorithms. The review emphasizes the importance of data preprocessing, feature engineering, and model interpretability in achieving reliable CKD prediction. The findings suggest that hybrid and ensemble models outperform traditional methods, offering enhanced prediction accuracy and aiding clinical decision-making. Future research directions include the integration of explainable AI, real-time monitoring, and personalized predictive systems for improved CKD management.},
        keywords = {Deep Learning (DL), Machine Learning (ML), Support Vector Machine (SVM).},
        month = {December},
        }