Predictive Modeling of Chronic Kidney Disease Progression with Ensemble Learning Techniques

Abstract

The present study aims to tackle the significant issue of prompt identification of chronic kidney disease (CKD), a highly prevalent and potentially fatal medical illness. Given the crucial function of the kidneys in maintaining homeostasis, we put forth a novel ensemble learning model to forecast the onset of chronic kidney disease (CKD). Utilizing an extensive dataset, the study employs ten carefully designed stages, covering data analysis, missing data management, normalization, and training of machine learning models. The model that we have proposed exhibits superior performance compared to the existing approaches, attaining a noteworthy accuracy rate of 98.74%. Additionally, it demonstrates a sensitivity rate of 100%, a specificity rate of 96.54%, and an F1 score of 99.02%. The visual representation of the confusion matrix effectively showcases the strong performance of the model. The results of this study indicate that our ensemble technique holds promise as a valuable tool for the prompt detection of chronic kidney disease (CKD). It has the potential to improve diagnostic accuracy in clinical settings and alleviate the financial burden associated with advanced CKD treatments.