Early detection of chronic kidney disease: comparative study with ensemble learning, traditional machine learning and neural network approaches

Abstract

Chronic Kidney Disease (CKD) is a common progressive disease, affecting millions of people around the world, and early detection has proved to be one of the best approaches for improving patient outcomes and reducing the burden on the healthcare system. This research paper will provide a comparative analysis of the early detection of the CKD through the standard clinical data. It compares three different machine learning paradigms systematically, including Traditional Machine Learning, Ensemble Learning and Neural Network approaches. The goal is to develop a strong classification model to differentiate people with CKD and healthy patients based on the features such as blood pressure, serum creatinine, hemoglobin etc. After all the data preparation and feature selection, various models of each paradigm were generated and tested. The outcomes prove that an ensemble-based model has performed better, providing a perfect balance between high accuracy, strong interpretability, and practical utility among the healthcare professionals. In this model, patients at risk are easily identified early, and appropriate and possibly life-saving procedures can be implemented.