CLASSIFICATION OF CHRONIC KIDNEY DISEASE USING THE XGBOOST METHOD
DOI:
10.54443/morfai.v5i5.4321Published:
2025-10-30Downloads
Abstract
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.
Keywords:
Chronic Kidney Disease XGBoost Kaggle Classification SMOTEReferences
REFERENCES
Abdillah, M., Sarira, B. T., Hidayat, A. N., Fauzan, A. N., Nurhidayat, R., Septiarini, A., & Puspitasari, N. (2025). Implementasi XGBoost dalam Klasifikasi Gagal Ginjal Kronis Menggunakan Dataset Chronic Kidney Disease. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 12(3). https://doi.org/10.35957/jatisi.v12i3.11546
Alghamdi, H. S., Amoudi, G., Elhag, S., Saeedi, K., & Nasser, J. (2021). Deep Learning Approaches for Detecting COVID-19 from Chest X-Ray Images: A Survey. IEEE Access, 9, 20235–20254. https://doi.org/10.1109/access.2021.3054484
Andryan, M. R., Fajri, M., & Sulistyowati, N. (2022). Komparasi Kinerja Algoritma Xgboost Dan Algoritma Support Vector Machine (Svm) Untuk Diagnosis Penyakit Kanker Payudara. JIKO (Jurnal Informatika Dan Komputer), 6(1), 1. https://doi.org/10.26798/jiko.v6i1.500
Anggraini, S., & Fadila, Z. (2022). Kualitas Hidup Pasien Gagal Ginjal Kronik Dengan Dialisis Di Asia Tenggara : a Systematic Review. Hearty, 11(1), 77. https://doi.org/10.32832/hearty.v11i1.7947
Anju Fauziah, & Julan Hernadi. (2025). Klasifikasi Data Tak Seimbang Menggunakan Algoritma Random Forest dengan SMOTE dan SMOTE-ENN. Teknomatika: Jurnal Informatika Dan Komputer, 17(2), 38–47. https://doi.org/10.30989/teknomatika.v17i2.1530
Artanto Halim, R., & Pratiwi, H. (2025). Application of K-Means Algorithm for Segmentation Analysis of Youtube Viewers in Indonesia-Ryan Artanto Halim et.al Application of K-Means Algorithm for Segmentation Analysis of Youtube Viewers in Indonesia. Jurnal INFOKUM, 13(3), 854–862. https://doi.org/10.58471/infokum.v13i03
Chittora, P., Chaurasia, S., Chakrabarti, P., Kumawat, G., Chakrabarti, T., Leonowicz, Z., Jasinski, M., Jasinski, L., Gono, R., Jasinska, E., & Bolshev, V. (2021). Prediction of Chronic Kidney Disease - A Machine Learning Perspective. IEEE Access, 9, 17312–17334. https://doi.org/10.1109/ACCESS.2021.3053763
Dungga, E. F., Indiarti, Y., Kedokteran, P. S., Kedokteran, F., & Negeri, U. (2023). 1 , 2 1,2. 20, 993–1000.
Indonesia, P. K. (2017). Profil Kesehatan Indonesia. Kementerian Kesehatan Republik Indonesia, Jakarta.
Issn, P. (2021). Implementasi Algoritma Decision Tree Untuk Klasifikasi Produk Laris. Jurnal Ilmiah Ilmu Komputer, 7(2), 45–51.
Levey, A. S., Eckardt, K. U., Dorman, N. M., Christiansen, S. L., Hoorn, E. J., Ingelfinger, J. R., Inker, L. A., Levin, A., Mehrotra, R., Palevsky, P. M., Perazella, M. A., Tong, A., Allison, S. J., Bockenhauer, D., Briggs, J. P., Bromberg, J. S., Davenport, A., Feldman, H. I., Fouque, D., … Winkelmayer, W. C. (2020). Nomenclature for kidney function and disease: report of a Kidney Disease: Improving Global Outcomes (KDIGO) Consensus Conference. Kidney International, 97(6), 1117–1129. https://doi.org/10.1016/j.kint.2020.02.010
Nageswari, A., Jyothi, U., Divya, G., Ammannamma, T., & Usha, V. (2024). Water Quality Classification using XGBoost method. ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications, 302–306. https://doi.org/10.1109/ICCCMLA63077.2024.10871422
Niazkar, M., Menapace, A., Brentan, B., Piraei, R., Jimenez, D., Dhawan, P., & Righetti, M. (2024). Applications of XGBoost in water resources engineering: A systematic literature review (Dec 2018–May 2023). Environmental Modelling and Software, 174(October 2023). https://doi.org/10.1016/j.envsoft.2024.105971
Pandey, V., Lilhore, U. K., & Walia, R. (2025). A systematic review on cardiovascular disease detection and classification. Biomedical Signal Processing and Control, 102(December 2024), 107329. https://doi.org/10.1016/j.bspc.2024.107329
Prastiwi, H., Pricilia, J., & Raswir, E. (2022). Jurnal Informatika Dan Rekayasa Komputer (JAKAKOM) Implementasi Data Mining Untuk Menentuksn Persediaan Stok Barang Di Mini Market Menggunakan Metode K-Means Clustering. Informatika Dan Rekayasa Komputer (JAKAKOM), 1(2), 141–128.
Rizal, M., Syahaf, M. Z., Priyambodo, S. R., & Rhamdani, Y. (2023). Optimasi Algoritma Naïve Bayes Menggunakan Forward Selection Untuk Klasifikasi Penyakit Ginjal Kronis. Naratif : Jurnal Nasional Riset, Aplikasi Dan Teknik Informatika, 5(1), 71–80. https://doi.org/10.53580/naratif.v5i1.200
Rovin, B. H., Adler, S. G., Barratt, J., Bridoux, F., Burdge, K. A., Chan, T. M., Cook, H. T., Fervenza, F. C., Gibson, K. L., Glassock, R. J., Jayne, D. R. W., Jha, V., Liew, A., Liu, Z. H., Mejía-Vilet, J. M., Nester, C. M., Radhakrishnan, J., Rave, E. M., Reich, H. N., … Floege, J. (2021). KDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases. Kidney International, 100(4), S1–S276. https://doi.org/10.1016/j.kint.2021.05.021
Sekar Setyaningtyas, Indarmawan Nugroho, B., & Arif, Z. (2022). Tinjauan Pustaka Sistematis Pada Data Minin: Studi Kasus Algoritma K-Means Clustering. Jurnal Teknoif Teknik Informatika Institut Teknologi Padang, 10(2), 52–61.
Sudarsono, B. G., Leo, M. I., Santoso, A., & Hendrawan, F. (2021). Analisis Data Mining Data Netflix Menggunakan Aplikasi Rapid Miner Analysis Data Mining Netflix Data Using The Rapid Miner Application. Journal of Business and Audit Information Systems, 4(1), 13–21.
Wijayanti, R. A., Furqon, M. T., & Adinugroho, S. (2018). Penerapan Algoritme Support Vector Machine Terhadap Klasifikasi Tingkat Risiko Pasien Gagal Ginjal. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Koputer, 2(10), 3500–3507.
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