Authors - Arun Kumar S, Niveditha S, Vikas K B, Varshini C, Hemalatha H U Abstract - Improved patient outcomes and effective management of Chronic Kidney Disease (CKD) depend on early detection, making it a major worldwide health concern. This study presents a hybrid machine learning model that combines Random Forest and LightGBM classifiers in order to accurately predict CKD stages. The dataset from Kaggle was used to build the model, and SMOTE was used to handle class imbalances in addition to feature engineering and data preprocessing. With regard to test data, the model's accuracy was 97.7%. Enhancing its actual clinical utility, a web-based tool was also developed to enable real-time forecasts of CKD stage based on patient inputs. Using a mixed-method approach that included more than 1600 participants' medical examinations and surveys With the integration of real-time clinical data and strong predictive models among its potential enhancements, the proposed approach provides a robust and easily utilized tool for clinical applications.
