Mukta, Jannatun NoorShahriar, Md RummanZami, Hasan SarwarNabil, Saib SalehRifat, Mahedi MostafaHasan, Tasnimul2026-03-312026-03-3120252025-12ID 24241284ID 22101335ID 23241068ID 22101739ID 23241061http://hdl.handle.net/10361/27681Cataloged from PDF version of thesis.Includes bibliographical references (pages 80-84).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.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.91 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.CKDChronic kidney diseaseEarly detectionAINeural networkDeep learningKidneys--Diseases.Kidneys.Thesis