CLASSIFICATION OF CHRONIC KIDNEY DISEASE USING THE XGBOOST METHOD
Main Article Content
Doli Indra Harahap
Damayanti
Chronic Kidney Disease (CKD) is a global health problem that requires early diagnosis to prevent serious complications. This study aims to develop a CKD classification model using the XGBoost algorithm on the Kidney Disease dataset with 16,432 samples, which includes clinical features such as smoking status, diabetes mellitus, hypertension, BMI, and CRP. The method includes data preprocessing (missing value handling, categorical coding, normalization), dataset splitting (80% training, 20% testing), and hyperparameter optimization through grid search with 3-fold cross-validation. The XGBoost model was configured with optimal parameters (subsample 1.0, n_estimators 200, max_depth 6, learning_rate 0.2, colsample_bytree 0.8) for multi-class classification of CKD risk. The evaluation results showed an accuracy of 87.33%, with a macro avg F1-score of 0.87, a precision of 0.87, and a recall of 0.87, confirming balanced performance across all classes. Important features such as CRP and diabetes mellitus contribute significantly, supporting clinical interpretability. The conclusion of this study indicates that XGBoost is effective for CKD diagnosis, with potential integration into electronic health systems for mass screening, although further validation is needed on local Indonesian data. This research contributes to machine learning-based diagnostic innovations to reduce the burden of CKD.
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