Predicting the Stages of Chronic Kidney Disease Using Machine Learning Approach

Abstract

A condition due to which the kidneys cannot perform its regular function of filtering blood refer to Chronic kidney disease; nowadays people belonging to different ages are suffering and coherently increased the death rate of related patients, premature of diagnosis. Kidney Disease has become a major problem in the general public all over the world, as it damages the kidney. Kidney failureis measured by GFR (Glomerular Filtration Rate). In this research work, various supervised machine learning algorithms are used to predict and classify Chronic Kidney Disease and non-Chronic Kidney Disease. The dataused for this work has been collected from the machine learningrepository and on these datasetSVM, Navie Bayes, Decision Trees and K-NN models has been applied. The system has shown better results in classifying Chronic Kidney Disease and non-Chronic Kidney Disease. The results of classifiers are compared. The study concludes that among all the classifiers, the SVM and Decision Tree have performed better than other classifiers. Stage detection is also done by using different attributes of the dataset and proposed a system to detect and identify the different grades of chronic kidney Disease.