Vanshika Goel
Vol. 18, Jul-Dec 2024
Abstract:
Chronic kidney disease is a significant medical condition that requires ongoing monitoring and early detection to prevent negative outcomes. This paper presents a novel research of using machine learning techniques on real-time clinical datasets for early CKD detection and progression tracking. In order to produce accurate insights into the onset and course of chronic kidney disease (CKD), predictive models are constructed utilizing a wide variety of clinical tests and patient data. The suggested approach combines test results with medical histories to evaluate longitudinally collected data in an efficient manner. Through the use of ensemble techniques, this study enhances the efficacy of machine learning algorithms for early CKD detection and progression monitoring. By integrating several clinical data sources, these methods enhance interpretability and accuracy, enabling medical professionals to maximize patient outcomes and treatment.
DOI: http://doi.org/10.37648/ijrmst.v18i01.004