“Forecasting Chronic Kidney Disease using the Decision tree and Random Forest Algorithms in Machine learning”

Author Reg. ID: TIJER_106718

Published Paper Id: TIJERA001035

Published In: Volume 10 Issue 7, July-2023

DOI: http://doi.one/10.1729/Journal.35424

Abstract: One of the most significant global health problems is chronic kidney disease. It has a high morbidity and death rate. Patients frequently miss the diagnosis of CKD because there are no symptoms in the early stage of the condition. The early diagnosis of CKD in the critical state enables patients to get immediate treatment, which slows the disease’s further development. The application of the machine learning techniques in health care for disease categorization and prediction has increased as a result of the availability of pathology data. The classification of CKD using a machine learning model by SVM (Support Vector Machine) proposed in this paper. We trained around 24 features of datasets among 19 features were tested and achieved 93.75% accuracy.

Keywords: chronic kidney disease (CKD), Support Vector Machine (SVM), Machine Learning (ML).

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Page No: 192-196

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Research Area: Science and Technology

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJERA001035

Published Paper PDF: https://tijer.org/TIJER/papers/TIJERA001035

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