Abstract: This project aims to develop a system for predicting Chronic Kidney Disease (CKD) using machine learning method. Specifically, the proposed system employs an XGBoost to predict CKD. The dataset used for training and testing the models is the Chronic Kidney Disease dataset from the UCI Machine Learning Repository.The proposed system also built a web application using Flask framework where the users can enter the details and predict whether the CKD is there or not, which makes the system easier and accessible to every individual. The study contributes to the field of medical diagnosis and highlights the potential of using machine learning techniques for improving CKD prediction This user-friendly interface makes the system practical for both healthcare professionals and individuals seeking early diagnosis.By leveraging machine learning, this study contributes to the field of medical diagnosis, demonstrating the potential of ANN models in improving CKD prediction accuracy. The proposed system can assist in early detection, thereby facilitating timely medical intervention and improving patient outcomes
Keywords: XGBoost UCI machine,Chronic kidney disease,random forest algorithm
Copyright Form