International Journal of Scientific Research and Engineering Development

Chronic Kidney Disease (CKD) is a serious global health problem that often remains undetected until advanced stages, leading to complications such as hypertension, anemia, and kidney failure. Early identification of CKD is important for improving treatment outcomes and reducing health risks. This study proposes a machine learning–based approach for early CKD prediction using clinical health data. The system performs data preprocessing and feature selection before applying two ensemble learning models: Random Forest and AdaBoost. These models are evaluated using standard performance metrics to determine their effectiveness in classifying CKD cases. The model is integrated into a Streamlit-based web application that enables real-time CKD risk assessment. The proposed system offers an accurate, interpretable, and scalable solution that can support early diagnosis and assist healthcare professionals in clinical decision-making.

Mr.Aashish Janardhan Mallah, Mr.Abhishek Jagdish Mishra, Mr.Pritish Ashok Shetty, Mr.Kapil Deendayal Mewati, Mr.Sandesh Patil,"Chronic Kidney Disease Prediction Using ML" International Journal of Scientific Research and Engineering Development, V9(2): Page(1002-1007) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.