HYBRID MODEL FOR PREDICTION OF CHRONIC KIDNEY DISEASE USING MACHINE LEARNING

Abstract

This study focuses on using machine learning techniques to detect chronic kidney disease (CKD) early, as timely intervention significantly impacts patient outcomes.CKD is often misdiagnosed until it worsens, leading to poorer treatment results.By leveraging advanced machine learning algorithms like Random Forest, Gradient Boosting, and others, the study aims to develop accurate predictive models for early CKD detection.Automation in diagnostics is crucial for expediting and simplifying the process, enhancing accessibility, and overcoming manual limitations.Ultimately, this research aims to improve patient outcomes, reduce healthcare costs, and enhance the quality of life for CKD patients through early detection and tailored interventions.The study emphasizes the silent nature of early-stage CKD and the need for data-driven approaches to evaluate risk factors.By comparing various machine learning classifiers and emphasizing automation, it aims to enhance diagnostic accuracy and accessibility.Ultimately, this research aims to revolutionize early CKD detection, leading to better patient outcomes and a higher standard of living.