Machine Learning Methodology for Prediction of Chronic Kidney Disease

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.