Prediction of Chronic Disease in Kidneys Using Machine Learning Classifiers

Abstract

chronic kidney disease (CKD) is a diagnosis that occurred in kidney which is also called as chronic renal disease. Chronic Kidney Disease means that if a person is suffering from CKD, then that person's kidney is not functioning properly since itcould be damaged and cannot purify the blood as like the normal kidney. Chronic kidney disease has become a common disease in most of the countries and there is no cure for this disease if itprogresses to its final's stages. The only way to live is to rely ondialysis or kidney transplantation but these processes require more time, and it is costly. So, to overcome these problems machine learning techniques are used to diagnosis the disease on time. In this paper we have used four machine learning algorithms which are Support vector machine classifier (SVM), K-Nearest Neighbor algorithm (KNN), Random Forest algorithm and lastly decision tree algorithm. The dataset used to train these algorithms is taken from UCI repository. Data preprocessing techniques are applied to the dataset so that after training these algorithms the accuracy in predicting CKD increases.

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