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@article{155328,
        author = {Rohini Jadhav and Madhavi Mane and Prasanna Garole and Prafull Tripathi and Nishant Kumar},
        title = {Diagnostic Prediction of Chronic Kidney/Renal Disease using XGBoost Machine Learning Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {1},
        pages = {599-605},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155328},
        abstract = {Chronic Kidney Disease (CKD) or Chronic Renal Disease (CRD) is a general term for multiple heterogeneous diseases in the kidneys that occurs when kidneys are damaged and cannot filter blood properly. Over time this could result in the development of severe cardiovascular disease, End-Stage Renal Disease (ESRD), and may even lead to death. The disease affects 10 per cent of the population worldwide and millions of lives are lost every year with conditions worse particularly in the developing nations due to lack of early diagnosis and affordable treatment. Since CKD depends on various factors like sugar, specific gravity, anemia etc., Machine-learning based systems can be developed based on these factors so that early prediction and diagnostic results can be achieved in a fairly short time, thereby allowing doctors to treat patients more effectively. This paper proposes utilization of various techniques such as data pre- processing, data visualization, feature-selection algorithms (SelectKBest, chi2) and prediction algorithm (XGBoost-eXtreme Gradient Boosting) to determine CKD. Eventually RandomisedSearchCV is used for tuning the hyperparameters.},
        keywords = {chi2, Chronic Kidney Disease, End-Stage Renal Disease, SelectKBest, RandomisedSearchCV, , XGBoost},
        month = {},
        }