Machine Learning for Early Detection of Chronic Kidney Disease

Abstract

Chronic Kidney Disease (CKD) is a progressive condition that often remains asymptomatic in its early stages, making timely detection essential for effective treatment and prevention of severe complications. This paper presents a Machine Learning-based framework for the early detection of Chronic Kidney Disease using clinical and laboratory data. The proposed system analyzes key health indicators such as blood pressure, serum creatinine, hemoglobin levels, and other relevant biomarkers. Data preprocessing techniques, including normalization, handling missing values, and feature selection, are applied to improve data quality and model performance. Various machine learning models, including Decision Trees, Support Vector Machines, and ensemble methods, are evaluated to identify the most effective predictive approach. The framework is further enhanced using hybrid techniques that combine predictive modeling with optimization strategies 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 healthcare analytics strengthens the robustness and scalability of the model. The study highlights the potential of machine learning techniques in enabling early diagnosis and supporting clinical decision-making in resource-constrained healthcare environments.