EARLY PREDICTION OF CHRONIC KIDNEY DISEASE USING AN ENSEMBLE MACHINE LEARNING-BASED CLINICAL DECISION SUPPORT SYSTEM

Kancharla Anitha; B. Basaveswara Rao

doi:10.29121/shodhkosh.v7.i13s.2026.8433

Authors

Kancharla Anitha Department of Computer Science and Engineering, Acharya Nagarjuna University, Amaravathi, Andhra Pradesh, India
B. Basaveswara Rao Department of Computer Science and Engineering, Acharya Nagarjuna University, Amaravathi, Andhra Pradesh, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i13s.2026.8433

Keywords:

Chronic Kidney Disease (CKD), Random Forest Classifier, Feature Engineering, Cross-Validation, Early Prediction

Abstract [English]

Chronic Kidney Disease (CKD) is a progressive medical condition that requires early prediction to reduce complications and healthcare costs. This study presents an ensemble-based clinical decision support system for early CKD prediction using two public datasets: UCI-CKD and CKD-15. A structured preprocessing pipeline was implemented. A fine-tuned Random Forest classifier was employed, and a feature ablation study was conducted to analyze the contribution of numerical, categorical, and clinically selected feature groups. Model performance was evaluated using stratified k-fold cross-validation (3-, 5-, and 10-fold). The model achieved near-perfect performance on the UCI-CKD dataset with 99.99% accuracy under cross-validation. On the CKD-15 dataset, the model achieved a maximum accuracy of 92.41%, with numerical features providing the highest predictive performance. The results demonstrate strong classification capability and good generalization for early CKD prediction.

References

Ahmed, Md Razu, Md Abdur Rakib, Abu Bakar Shiddik, and Md Shamim Reza. 2025. “Identification of Predisposing Risk Factors for Chronic Kidney Disease and Optimizing Disease Prediction Using a Stacking Machine Learning Algorithm.” International Journal of Statistical Sciences 25 (2): 1–32. https://doi.org/10.3329/ijss.v25i2.85732.

Ahmed, R. M., & Alshebly, O. Q. (2019). Prediction and Factors Affecting of Chronic Kidney Disease Diagnosis using Artificial Neural Networks Model and Logistic Regression Model. IRAQI JOURNAL OF STATISTICAL SCIENCES, 16(1), 140–159. https://doi.org/10.33899/iqjoss.2019.0164186

Alsekait, D. M., Saleh, H., Gabralla, L. A., Alnowaiser, K., El-Sappagh, S., Sahal, R., & El-Rashidy, N. (2023). Toward comprehensive chronic kidney disease prediction based on ensemble deep learning models. Applied Sciences, 13(6), 3937. https://doi.org/10.3390/app13063937

Ashafuddula, N. I. M., Islam, B., & Islam, R. (2023). An intelligent diagnostic system to analyze Early-Stage chronic kidney disease for clinical application. Applied Computational Intelligence and Soft Computing, 2023, 1–17. https://doi.org/10.1155/2023/3140270

Bai, Q., Su, C., Tang, W., & Li, Y. (2022). Machine learning to predict end stage kidney disease in chronic kidney disease. Scientific Reports, 12(1), 8377. https://doi.org/10.1038/s41598-022-12316-z

Bilal, A., Alzahrani, A., Almuhaimeed, A., Khan, A. H., Ahmad, Z., & Long, H. (2024). Advanced CKD detection through optimized metaheuristic modeling in healthcare informatics. Scientific Reports, 14(1), 12601. https://doi.org/10.1038/s41598-024-63292-5

Gogoi, P., & Valan, J. A. (2024). Machine learning approaches for predicting and diagnosing chronic kidney disease: current trends, challenges, solutions, and future directions. International Urology and Nephrology, 57(4), 1245–1268. https://doi.org/10.1007/s11255-024-04281-5

Gupta, Rupal, Shalini Gambhir, Ondrej Krejcar, Achin Jain, Arvind Panwar, Shakir Khan, and Hamidreza Namazi. 2026. “Data-driven Explainable Chronic Kidney Disease Detection Using RF Based Data Imputation and Meta-ensemble Learning.” Scientific Reports, March. https://doi.org/10.1038/s41598-026-41425-2.

Habiba, S. U., Tasnim, F., Chowdhury, M. S. H., Islam, M. K., Nahar, L., Mahmud, T., Kaiser, M. S., Hossain, M. S., & Andersson, K. (2024). Early Prediction of Chronic Kidney Disease Using Machine Learning Algorithms with Feature Selection Techniques. In Communications in computer and information science (pp. 224–242). https://doi.org/10.1007/978-3-031-68639-9_14

Ilyas, H., Ali, S., Ponum, M., Hasan, O., Mahmood, M. T., Iftikhar, M., & Malik, M. H. (2021). Chronic kidney disease diagnosis using decision tree algorithms. BMC Nephrology, 22(1), 273. https://doi.org/10.1186/s12882-021-02474-z

Islam, M. A., Majumder, M. Z. H., & Hussein, M. A. (2023). Chronic kidney disease prediction based on machine learning algorithms. Journal of Pathology Informatics, 14, 100189. https://doi.org/10.1016/j.jpi.2023.100189

K. M. S. A. Rabby, R. Mamata, M. A. Laboni, Ohidujjaman and S. Abujar, "Machine Learning Applied to Kidney Disease Prediction: Comparison Study," 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India, 2019, pp. 1-7, doi: 10.1109/ICCCNT45670.2 019.8944799.

Kumar, A., Pandey, R. K., & Srivastava, P. K. (2025). Hybrid Ensemble Learning Model for Chronic Kidney Disease Prediction. Vascular and Endovascular Review, 8(4s), 292-304.

Kuo, C., Chang, C., Liu, K., Lin, W., Chiang, H., Chung, C., Ho, M., Sun, P., Yang, R., & Chen, K. (2019). Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning. Npj Digital Medicine, 2(1), 29. https://doi.org/10.1038/s41746-019-0104-2

Levin, Adeera, Paul E. Stevens, Rudy W. Bilous, Josef Coresh, Angel LM De Francisco, Paul E. De Jong, Kathryn E. Griffith et al. "Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease." Kidney international supplements 3, no. 1 (2013): 1-150.

Liu, P., Sawhney, S., Heide-Jørgensen, U., Quinn, R. R., Jensen, S. K., Mclean, A., Christiansen, C. F., Gerds, T. A., & Ravani, P. (2024). Predicting the risks of kidney failure and death in adults with moderate to severe chronic kidney disease: multinational, longitudinal, population based, cohort study. BMJ, 385, e078063. https://doi.org/10.1136/bmj-2023-078063

M. M. S. Raihan et al., "Chronic Renal Disease Prediction using Clinical Data and Different Machine Learning Techniques," 2021 2nd International Informatics and Software Engineering Conference (IISEC), Ankara, Turkey, 2021, pp. 1-5, doi: 10.1109/IISEC54230.2021.9672365.

Moreno-Sánchez, P. A. (2023). Data-Driven Early Diagnosis of Chronic Kidney Disease: Development and evaluation of an explainable AI model. IEEE Access, 11, 38359–38369. https://doi.org/10.1109/ access.2023.3264270

Ogunleye and Q. -G. Wang, "XGBoost Model for Chronic Kidney Disease Diagnosis," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 17, no. 6, pp. 2131-2140, 1 Nov.-Dec. 2020, doi: 10.1109/TCBB.2019.2911071.

P. Chittora et al., "Prediction of Chronic Kidney Disease - A Machine Learning Perspective," in IEEE Access, vol. 9, pp. 17312-17334, 2021, doi: 10.1109/ACCESS.2021.3053763.

Pal, S. (2022). Chronic kidney disease prediction using machine learning techniques. Biomedical Materials & Devices, 1(1), 534–540. https://doi.org/10.1007/s44174-022-00027-y

Rabie El Kharoua. (2024). Chronic Kidney Disease Dataset [Data set]. Kaggle. https://doi.org/10.347 40/KAGGLE/DSV/8658224

Rahman, Md Habibur, Mustafizur Rahaman, Yasin Arafat, Sk Rakib Ul Islam Rahat, Rahat Hasan, S M Tamim Hossain Rimon, and Sadia Afrin Dipa. 2025. “Artificial Intelligence for Chronic Kidney Disease Risk Stratification in the USA: Ensemble Vs. Deep Learning Methods.” Al-Kindipublishers.Org, August. https://doi.org/10.3299 6/bjns.2025.5.2.3.

Raihan, M. J., Khan, M. A., Kee, S., & Nahid, A. (2023). Detection of the chronic kidney disease using XGBoost classifier and explaining the influence of the attributes on the model using SHAP. Scientific Reports, 13(1), 6263. https://doi.org/10.1038/s41598-023-33525-0

Ramesh, B., & Rao, K. P. (2025). Intelligent Detection of Chronic Kidney Disease Using Optimized MLP Models and Feature Selection Techniques on the AP-CKD Dataset. IAENG International Journal of Computer Science, 52(10).

Revathy, S., Bharathi, B., Jeyanthi, P., & Ramesh, M. (2019). Chronic Kidney Disease Prediction using Machine Learning Models. International Journal of Engineering and Advanced Technology (IJEAT), 9(1), 6364–6367. https://doi.org/10.35940/ijeat.A2213.109119

Rubini, L., P. Soundarapandian, and P. Eswaran. 2015. Chronic Kidney Disease. UCI Machine Learning Repository. https://doi.org/10.24432/C5G020.

Singh, V., Asari, V. K., & Rajasekaran, R. (2022). A deep neural network for early detection and prediction of chronic kidney disease. Diagnostics, 12(1), 116. https://doi.org/10.3390/diagnostics12010116

Subasi, A., Alickovic, E., & Kevric, J. (2017). Diagnosis of chronic kidney disease by using random forest. In World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany (pp. 589–594). https://doi.org/10.1007/978-981-10-4166-2_89

Surekha, Y., Kodepogu, K. R., Kumari, G. L., Babu, N. R., Lanka, T., Volla, M. A., Pillutla, M., & Kari, A. (2023). Prediction of Chronic Kidney Disease with Machine Learning Models and Feature Analysis Using SHAP. Revue D Intelligence Artificielle, 37(2), 493–499. https://doi.org/10.18280/ria.370226

Thomas, Robert, Abbas Kanso, and John R. Sedor. "Chronic kidney disease and its complications." Primary care: Clinics in office practice 35, no. 2 (2008): 329-344.

U. Ekanayake and D. Herath, "Chronic Kidney Disease Prediction Using Machine Learning Methods," 2020 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2020, pp. 260-265, doi: 10.1109/MERCon50084.2020.9185249.

Variyar, A. R., and Karangara, R. (2026). Enhancing Complex Decision Making In Bpm Through Artificial Intelligence: A Systematic Examination. ShodhAI: Journal of Artificial Intelligence, 3(1), 29–36. https://doi.org/10.29121/shodhai.v3.i1.2026.72

Vetrithangam, D., Himabindu, Saranya, Neha, S., Naresh Kumar, P., Fathima, A., Ashok, B., & Akanksha, K. (2024). Improved RESNET models for chronic kidney disease prediction. Journal of Electrical Systems, 20(2s), 165–183. https://doi.org/10.52783/jes.1121

Wang, Y., Guan, Z., Hou, W., & Wang, F. (2021). TRACE: Early Detection of Chronic Kidney Disease Onset with Transformer-Enhanced Feature Embedding. In Lecture notes in computer science (pp. 166–182). https://doi.org/10.1007/978-3-030-93663-1_13

Xue, Ningning, Tiantian Bai, Xianjie Jia, and Xing Wei. 2026. “Early Detection of Chronic Kidney Disease Based on a SURD-enhanced Machine Learning Model.” Scientific Reports, February. https://doi.org/10.1038/s41598-026-41050-z.