Prognostic Modeling of Chronic Kidney Disease Progression: Bridging Mild and Severe Stages through a Machine Learning Approach

Abstract

Background and Aim: Chronic Kidney Disease (CKD) is a condition where the kidneys gradually lose their ability to function properly over time. It is into stages based on the severity of kidney damage and the level of kidney function. The objective of our study is to employ machine learning models for the prediction of Chronic Kidney Disease (CKD) progression. Methods: Our study is centered on the prediction of CKD progression from mild (I, II, III) to advanced stages (IV, V, VI). We utilized logistic regression with a lasso-penalized approach and random forest model for our predictive analysis. We assessed the significance of features using the Gini index derived from the random forest model. The performance of our models was evaluated based on the Area Under Receiver Operating Characteristic (AU-ROC), AU-Precision-Recall (PR) curves, recall, precision and accuracy. Results: Our study showcases remarkable predictive performance of CKD progression from milder (I, II, III) to severe stages (IV, V, VI). Random forest model achieved an accuracy of 85%, a recall rate of 86%, a precision rate of 83%, an AU-ROC score of 92%, and an AU-PR score of 83%. The logistic regression model exhibited an accuracy of 84%, a recall rate of 84%, a precision rate of 85%, an AU-ROC score of 92%, and an AU-PR score of 81%. Regarding variable importance, our model identifies creatinine as the most critical feature, followed by eGFR. Conclusion: Our findings indicate that machine learning models hold promise in predicting CKD progression with substantial discriminative capabilities, as evidenced by high AUROC curves. This suggests their potential utility in real-world clinical settings for identifying patients at risk of transitioning from mild to severe stages of CKD.