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.