Advanced Machine Learning Models for Prediction of Chronic Kidney Disease

Because of its rising incidence and effects on both people and medical infrastructure, chronic kidney disease (CKD) presents a serious threat to global healthcare. Early identification and efficient administration are essential for enhancing patient results and cutting medical expenses. Current mechanisms for CKD prediction is based on conventional statistical techniques and fundamental machine learning models, which frequently attain a moderate level of accuracy, and missing secure data processing and user-friendly interfaces. This project offers a web-based tool for CKD prediction. employing a group of supervised machine learning techniques, created utilizing the Django framework. The characteristics of the application OTP-verified user authentication, an admin module for model comparison and data exploration, as well as a user module for forecast. Different machine learning methods were assessed using a Kaggle dataset that included metrics including blood glucose levels, red blood cell count, packed cell volume, al-bumin, hemoglobin, serum creatinine, specific gravity, hypertension, and diabetes mellitus. 100% accuracy, precision, recall, and F1 score were attained by the Random Forest algorithm, demonstrating exceptional performance. The application is a useful tool for both patients and healthcare providers since it provides an easy-to-use interface for users to enter medical factors and receive CKD predictions. This demonstrates the promise of machine learning models in clinical settings and demonstrates how well they predict chronic kidney disease (CKD), especially Random Forest.