A Comparative Study on Chronic Kidney Disease using Different Machine Learning Techniques

Abstract

CKD (Chronic Kidney Disease) has become increasingly prevalent. Those with chronic kidney disease can suffer from the disease for the rest of their lives. There are two causes of this condition: kidney malignancy and reduced kidney function. It is possible to prevent patients from progressing to an end-stage of this disease that requires dialysis or surgery in the early stages. The likelihood of this happening can be increased if the disease is detected early and treated appropriately. Several machine learning approaches have been evaluated in this research for early detection of CKD. The topic of this research has received a great deal of attention. While this is the case, we are utilizing predictive modeling to enhance our approach. Through machine learning and predictive analytics, we can develop a collection of prediction models based on better measures of attributes. A supervised learning environment has been used to test different machine learning-based classifiers. As a result of the research, we can conclude that recent advances in machine learning, coupled with predictive analytics, could yield new treatments for kidney disease and other conditions