An Integrative Framework for Forecasting Chronic Renal Disease

This research provides a novel method of predicting chronic kidney disease (CKD) using a potent machine learning algorithm called the Gradient Boosting Classifier. A large dataset of kidney disease with both nominal and ordinal values is used in the investigation. The project goes beyond model building to include the construction of a specific website for CKD prediction in order to improve accessibility and usability. Through the use of the trained Gradient Boosting Classifier, t he website functions as an easy-to-use interface for users to enter pertinent medical data. The goal of this innovative web-based integration of predictive modeling is to close the knowledge gap between cutting-edge machine learning methods and real-world healthcare applications. This will enable early detection of chronic kidney disease (CKD) and better decision-making for both individuals and healthcare professionals. The study's findings show how well the suggested strategy uses machine learning to predict CKD while maintaining an online platform that is easy for users to navigate.