STACKING ENSEMBLE MACHINE LEARNING MODEL FOR EARLY DETECTION OF CHRONIC KIDNEY DISEASE IN INDONESIA - Agusviyanda; Hamdani; M. Khairul Anam; Agustin; M. Ikhsan Wibowo (Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, 2025)
Chronic Kidney Disease (CKD) is one of the major health problems that continues to rise and requires accurate early detection to prevent progression to end-stage renal failure. This study proposes a hybrid machine learning approach to automatically detect CKD by combining data balancing techniques, ensemble learning, and cross-validation. The dataset used was obtained from the Kaggle platform, consisting of 1,089 patient records, and was balanced using the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Three boosting algorithms—Adaboost, XGBoost, and LightGBM—were used as base models and combined through a stacking approach with Logistic Regression as the meta-classifier. Evaluation was conducted using a 5-fold cross-validation scheme with accuracy, precision, recall, and F1-score as performance metrics. The results show that the stacking model achieved an average accuracy of 99.40%, outperforming individual models (LightGBM: 98.87%; Adaboost: 98.76%; XGBoost: 98.61%) and exceeding the performance of several previous studies. These findings indicate that the stacking approach, when combined with SMOTE and cross-validation, significantly enhances classification performance for CKD detection.