Evaluation of Machine Learning Models in the Prediction of Chronic Kidney Disease

Abstract

Chronic Kidney Disease is a progressive condition that affects millions of people worldwide, often leading to kidney failure if not detected early. The early prediction of chronic kidney disease using machine learning models can significantly improve patient outcomes through timely intervention. This study evaluates the performance of various machine learning models, including Logistic Regression, Decision Tree, and Random Forest in predicting the presence of chronic kidney disease based on patient data. A dataset consisting of clinical features indicators was used for training and evaluation. The models were assessed based on accuracy, precision, recall, and F1-score. The results of this study showed that Random Forest outperformed the other models, although all models employed in the prediction demonstrated great accuracy in predicting the disease. This study demonstrates the potential of machine learning models in healthcare to aid in the early diagnosis of chronic kidney disease, thereby improving patient management and reducing the burden on healthcare systems. Further research should focus on integrating these models into clinical workflows for real-time prediction.