Chronic Kidney Disease Prediction Using Classifiers

Abstract

In today's era people are health conscious and pay more attention to health in spite of their workload and busy schedule. The field of biosciences have advanced to a larger extent and have generated large amounts of information from Electronic Health Records. This has given rise to the acute need of knowledge generation from this enormous amount of data. Data mining methods and machine learning play a major role in this aspect of biosciences. Chronic Kidney Disease (CKD) is a condition in which the kidneys are damaged and cannot filter blood. A family history of kidney diseases or failure, high blood pressure, type 2 diabetes may lead to CKD. This is a lasting damage to the kidney and chances of getting worse by time is high. The very common complications that results due to a kidney failure are heart diseases, anemia, bone diseases, high potassium and calcium. The worst-case situation leads to complete kidney failure and necessitates kidney transplant to live. An early detection of CKD can improve the quality of life to a greater extent. This calls for good prediction algorithms to predict CKD at an earlier stage. A wide range of machine learning algorithms are employed for the prediction of CKD. This work implements techniques like data pre-processing, exploratory analysis of data, model optimization, and by using various classifiers such as Logistic Regression, Naive Bayes and K-Nearest Neighbors (KNN) to predict CKD, the best classifier is chosen. Various experiments were carried out and the results confirm that Logistic Regression and Naive Bayes are good at predicting CKD in an early stage.