Prediction of Chronic Kidney Disease Using Machine Learning Technique

Abstract

One of the most crucial problems with artificial intelligence systems is thought to be the identification of correct kidney diseases through machine learning. Manual diagnosis for predicting the kidney disease by doctors is time consuming and may raise the workload on doctors. So, the developed system uses a machine learning technique for predicting the chronic kidney disease which may help the doctors in early prediction of the kidney disease. In order to diagnose chronic kidney disease four Machine Learning technique namely Naïve Bayes, Random Forest, Decision Tree and Support Vector Machine is used. Naive Bayes uses probability to forecast kidney disease, whereas decision trees are used to generate categorized reports for the disease. This system will compare the accuracy score of each Machine Learning technique. Hence, Random Forest gives the better performance compared to other classification methods with accuracy score of 98.75 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F1}=\mathbf{score}=\boldsymbol{99\%},\mathbf{ Precision}=\boldsymbol{99\%},\mathbf{Recall} =\boldsymbol{99\%}$</tex> . This paper shows the efficiency and accuracy of the predicted chronic kidney disease.

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