Enhancing machine learning-based forecasting of chronic renal disease with explainable AI.

ABSTRACT: Chronic renal disease (CRD) is a significant concern in the field of healthcare, highlighting the crucial need of early and accurate prediction in order to provide prompt treatments and enhance patient outcomes. This article presents an end-to-end predictive model for the binary classification of CRD in healthcare, addressing the crucial need for early and accurate predictions to enhance patient outcomes. Through hyperparameter optimization using GridSearchCV, we significantly improve model performance. Leveraging a range of machine learning (ML) techniques, our approach achieves a high predictive accuracy of 99.07% for random forest, extra trees classifier, logistic regression with L2 penalty, and artificial neural networks (ANN). Through rigorous evaluation, the logistic regression with

SUBMITTER: Singamsetty S 

PROVIDER: S-EPMC11623017 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature