Machine Learning Methodology for Prediction of Chronic Kidney Disease

Miss. Divya Ravinder Pogaku; Prof. Sneha Bohra

doi:10.36948/ijfmr.2023.v05i03.4172

Machine Learning Methodology for Prediction of Chronic Kidney Disease

Author(s)	Miss. Divya Ravinder Pogaku, Prof. Sneha Bohra
Country	India
Abstract	Chronic Kidney Disease (CKD) is a major health problem affecting millions of people worldwide. Early and accurate diagnosis of CKD is essential for successful management and treatment of the disease. In this paper, we propose a machine learning-based approach for diagnosing CKD using different classification algorithms. Our approach utilizes a combination of demographic data, medical history, and laboratory test results to predict CKD. We tested our approach using several machine learning algorithms, including decision trees, random forests, and support vector machines (SVM), and compared our results with traditional diagnostic methods. Our results show that SVM achieved the highest accuracy in diagnosing CKD, followed by decision trees and random forests. Our approach outperformed traditional diagnostic methods in terms of accuracy and reliability, demonstrating the potential of machine learning in improving CKD diagnosis. Our approach can be used to develop a computer-aided diagnosis system to assist clinicians in the early and accurate diagnosis of CKD, leading to better patient outcomes.
Keywords	CKD, SVM,Prediction
Field	Engineering
Published In	Volume 5, Issue 3, May-June 2023
Published On	2023-06-20
Cite This	Machine Learning Methodology for Prediction of Chronic Kidney Disease - Miss. Divya Ravinder Pogaku, Prof. Sneha Bohra - IJFMR Volume 5, Issue 3, May-June 2023. DOI 10.36948/ijfmr.2023.v05i03.4172
DOI	https://doi.org/10.36948/ijfmr.2023.v05i03.4172
Short DOI	https://doi.org/gsd48q

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International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Machine Learning Methodology for Prediction of Chronic Kidney Disease

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