Enhancing Early Prediction of Chronic Kidney Disease Using Hybrid Ensemble Machine Learning Approach

Chronic kidney Disease is a major global health challenge due to its silent progression and lack of early symptoms, often leading late- stage diagnosis. This paper present an enhanced machine learning framework using ensemble learning techniques for the early prediction of chronic kidney disease. The proposed model integrate traditional clinical parameter with additional lifestyle related risk factors such as smoking habits, dietary patterns, water in-taking, family history, and physical activity levels. It also incorporate chronic kidney disease stage identification to provide more detailed diagnosis insights. In the existing system has found some challenges such as missing values, imbalanced data, irrelevant features, noise, bias, and overfiffing , To overcome this issues the proposed framework applies robust data preprocessing and feature selection methods, ensuring that key health indicators are effectively utilized. The hybrid ensemble -based approach aims to improve prediction accuracy, precision, f1_score, reliability, and generalizability compared to single model systems. Experimental result on benchmark CKD dataset demonstrate the effectiveness of the proposed system predicting at early stages, while also highlighting the significant influence of lifestyle factors on disease risk prediction with proposed framework adaboost has to validate the average accuracy 98.9% for the early identification of Chronic kidney disease and early diagnosis support of healthcare system.