Volume 1 • Issue 1 • PP: 21-29 • 2020
Data Mining Algorithms for Kidney Disease Stages Prediction
Open Access & Copyright
© 2020 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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One of the most common health problems that correlated to serious complications is chronic kidney disease. Early detection and treatment can save it from progression. Machine learning is one tool that used historical data to improve future decision about prediction of chronic kidney disease. The aim of this work is to compare the performance of six different models based on accuracy, sensitivity, precision, recall. In this study, the experiments were conducted on 158 records downloaded from UCI repository. Six algorithms ( K-Nearest Neighbor, Naïve Bayes, Support Vector machine, Logistic Regression, Decision Tree, and Random Forest ) were implemented on data after preprocessing stage. Evaluation of models resulted in Naïve Bayes and Random Forest accuracy 100%, Sensitivity 100%, Specificity 100%, precision 100 %, Recall 100% respectively. It is concluded that Naïve Bayes and Random Forest are better than other models.
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