Diagnostic Prediction of Chronic Kidney/Renal Disease using XGBoost Machine Learning Algorithm

Rohini Jadhav; Madhavi Mane; Prasanna Garole; Prafull Tripathi; Nishant Kumar

Diagnostic Prediction of Chronic Kidney/Renal Disease using XGBoost Machine Learning Algorithm

Authors: Rohini Jadhav, Madhavi Mane, Prasanna Garole, Prafull Tripathi, Nishant Kumar

Unique Paper ID: 155328
Volume: 9
Issue: 1
PageNo: 599-605

Keywords: chi2 Chronic Kidney Disease End-Stage Renal Disease SelectKBest RandomisedSearchCV XGBoost

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.

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ISSN: 2349-6002
Volume: 9
Issue: 1
PageNo: 599-605

Diagnostic Prediction of Chronic Kidney/Renal Disease using XGBoost Machine Learning Algorithm

Available:https://ijirt.org/Article?manuscript=155328

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