Prediction and modeling of chronic kidney disease (CKD) progression using machine learning algorithms.

Abstract

In this work, we created an accurate and robust model for predicting chronic kidney disease (CKD) in a difficult situation with three majority classes. This helps patients avoid complications and improves their health. To achieve this objective, we employed efficient machine learning methods (K-Nearest-Neighbors, Decision Trees, Random Forest, Support Vector Machine, XGBoost, Logistic Regression) and retrieved data from medical records of the nephrology consulting service in Bejaia. The classifier's performance was compared based on accuracy rate. The Random Forest application achieved the greatest classification rate of 74% using the train/test evaluation approach.