Early Detection of Chronic Kidney Disease Using Machine Learning Models

Abstract

Chronic Kidney Disease (CKD) is a progressive condition that can lead to severe health complications, including kidney failure. Early detection is crucial to prevent disease progression and improve patient outcomes. In this study, we explore predictive modeling techniques for CKD detection using machine learning algorithms. Various clinical and laboratory parameters, such as serum creatinine, glomerular filtration rate (GFR), blood pressure, and proteinuria, are analyzed to identify key risk factors. Data preprocessing, feature selection, and model optimization techniques are employed to enhance predictive accuracy. The models, including logistic regression, decision trees, random forests, and deep learning approaches, are evaluated based on accuracy, sensitivity, and specificity. The results indicate that machine learning can effectively predict CKD at early stages, enabling timely intervention and personalized treatment plans. Future research should focus on integrating real-time data and improving model interpretability for clinical applications.