Kidney Disease Prediction Using Machine Learning

Abstract

Machine learning has earned a remarkable position in healthcare sector because of its capability to enhance the disease prediction in healthcare sector. Artificial intelligence and Machine learning techniques are being used in healthcare sector. Nowadays, kidney disease (KD) is becoming a major public health problem worldwide. It is increasing day by day because of not maintaining proper food habits, drinking less amount of water and lack of health consciousness. For that reason, there should have one or more approaches that can effectively keep tracking and monitoring people‟s kidney and health condition in an application view. Here, we have proposed an approach for real time kidney disease prediction, monitoring and application (KDPMA). Our aim is to develop an optimized and efficient machine learning (ML) application that can effectively recognize and predict the condition of chronic kidney disease. In this work, ten most important machine learning classification techniques were considered for predicting chronic kidney disease. In this process, the data has been divided into two sections. In one section train dataset got trained and another section got evaluated by test dataset. The analysis results show that Decision Tree Classifier and Gaussian Naïve Bayes achieved highest performance than the other classifiers, obtaining the F1 measure of 1.0. In future, we will make an application based on the best output results classifier technique to predict Kidney Disease.