Machine Learning for Early Detection of Chronic Kidney Disease

Abstract

Chronic Kidney Disease (CKD) is a progressive condition that often remains undetected until advanced stages, leading to severe health complications and increased mortality. This paper presents a Machine Learning-based framework for the early detection of Chronic Kidney Disease using clinical and laboratory data. The proposed system employs supervised learning algorithms to analyze key health indicators such as blood pressure, serum creatinine, hemoglobin levels, and other relevant biomarkers. Feature selection techniques are incorporated to identify the most significant attributes, thereby improving model efficiency and accuracy. Various machine learning models, including Decision Trees, Support Vector Machines, and ensemble methods, are evaluated to determine optimal performance. The framework is further enhanced through hybrid approaches that combine predictive modeling with optimization techniques to improve generalization. Experimental results demonstrate that the proposed system achieves high accuracy and reliability in early CKD detection. Additionally, integration with prior research in disease prediction and ensemble learning strengthens the robustness of the model. The study highlights the potential of machine learning techniques in enabling timely diagnosis and supporting clinical decision-making, particularly in resource-constrained healthcare environments.