التعلّم الآلي القابل للتعميم والقابل للتفسير للكشف المبكر عن مرض الكِلى المزمن

Ahlquist, K. D., Sugden, L. A., & Ramachandran, S. (2023). Enabling interpretable machine learning for biological data with reliability scores. PLOS Computational Biology, 19(5), e1011175. https://doi.org/10.1371/journal.pcbi.1011175

Alabi, R. O., Almangush, A., Elmusrati, M., Leivo, I., & Mäkitie, A. A. (2023). Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP. Scientific Reports, 13, 8984. https://doi.org/10.1038/s41598-023-35795-0

Bikbov, B., Purcell, C. A., Levey, A. S., Smith, M., Abdoli, A., Abebe, M., … GBD Chronic Kidney Disease Collaboration. (2020). Global, regional, and national burden of chronic kidney disease, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 395(10225), 709–733. https://doi.org/10.1016/S0140-6736(20)30045-3

Chen, F., et al. (2023). Prediction and diagnosis of chronic kidney disease development and progression using machine-learning: Protocol for a systematic review and meta-analysis of reporting standards and model performance. PLOS ONE, 18(9), e0278729. https://doi.org/10.1371/journal.pone.0278729

Chen, H., Huang, Y., & Chen, L. (2025). Ensemble machine learning for predicting renal function decline in chronic kidney disease: Development and external validation. Frontiers in Medicine, 12, 1598065. https://doi.org/10.3389/fmed.2025.1598065

Dritsas, E., Sideris, A., & Nikita, K. S. (2022). Machine learning techniques for chronic kidney disease prediction. Big Data and Cognitive Computing, 6(3), 98. https://doi.org/10.3390/bdcc6030098

Fujii, M., Ohno, Y., Ikeda, A., Godai, K., Li, Y., Nakamura, Y., … Kabayama, M. (2023). Current status of the rapid decline in renal function due to diabetes mellitus and its associated factors: Analysis using the National Database of Health Checkups in Japan. Hypertension Research, 46(5), 1075–1089. https://doi.org/10.1038/s41440-023-01185-2

He, J., et al. (2025). Identification and validation of an explainable early-stage chronic kidney disease prediction model: A multicenter retrospective study. eClinicalMedicine, 84, 103286. https://doi.org/10.1016/j.eclinm.2025.103286

Iwagami, M., et al. (2024). Estimating and predicting the rate of kidney function decline over 10 years in the general population. Kidney360, 5(12), 1862–1870. https://doi.org/10.34067/KID.0000000608

Jager, K. J., Kovesdy, C., Langham, R., Rosenberg, M., Jha, V., & Zoccali, C. (2023). A single number for advocacy and communication worldwide more than 850 million individuals have kidney diseases. The Lancet, 401(10380), 786–788. https://doi.org/10.1016/S0140-6736(23)00258-2

Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), Article 195. https://doi.org/10.1186/s12916-019-1426-2

Khafaga, D. S., et al. (2025). Enhanced early chronic kidney disease prediction using hybrid waterwheel plant algorithm for deep neural network optimization. Scientific Reports, 15, 26382. https://doi.org/10.1038/s41598-025-26382-6

Li, J., et al. (2025). Machine learning models for predicting short-term progression in patients with stage 4 chronic kidney disease: A multi-center validation study. Scientific Reports, 15, 39285. https://doi.org/10.1038/s41598-025-23037-4

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.

Oh, S. W., et al. (2024). Machine learning–based prediction of chronic kidney disease after nephrectomy. BMC Medical Informatics and Decision Making, 24, 85. https://doi.org/10.1186/s12911-024-02473-8

Okita, J., et al. (2024). Risk stratification of chronic kidney disease using machine learning models. BMC Nephrology, 25, 101. https://doi.org/10.1186/s12882-024-03527-9

Raihan, M. J., et al. (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, 6263. https://doi.org/10.1038/s41598-023-33525-0

Rank, N., et al. (2020). Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance. npj Digital Medicine, 3, 139. https://doi.org/10.1038/s41746-020-00346-8

Sakoi, N., Mori, Y., Tsugawa, Y., Tanaka, J., & Fukuma, S. (2024). Early-stage chronic kidney disease and related health care spending. JAMA Network Open, 7(1), e2351518. https://doi.org/10.1001/jamanetworkopen.2023.51518

Salakapuri, R., Terlapu, P. V., Kalidindi, K. R., et al. (2025). Stacking ensemble model for predicting chronic kidney disease in the Uddanam region of India with unknown etiology. Scientific Reports, 15, 39517. https://doi.org/10.1038/s41598-025-23239-w

Shin, D. H., et al. (2025). Ensemble learning approaches for early prediction of chronic kidney disease based on polysomnographic phenotype analysis. npj Digital Medicine, 8, Article 675. https://doi.org/10.1038/s41746-025-02039-6

Takkavatakarn, K., Oh, W., Cheng, E., et al. (2023). Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4. BMC Nephrology, 24, 376. https://doi.org/10.1186/s12882-023-03424-7

Yoshizaki, Y., et al. (2024). Development of a machine learning tool to predict the risk of incident chronic kidney disease using health examination data. Frontiers in Public Health, 12, 1495054. https://doi.org/10.3389/fpubh.2024.1495054

Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018). Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLOS Medicine, 15(11), e1002683. https://doi.org/10.1371/journal.pmed.1002683

Zhang, B., et al. (2025). Unveiling the effect of urinary xenoestrogens on chronic kidney disease in adults: A machine learning model. Ecotoxicology and Environmental Safety, 292, 117945. https://doi.org/10.1016/j.ecoenv.2025.117945

Mosbah, M., Blg, A., & Qarash, S. (2023). The impact of pre-processing techniques on Arabic text categorization using logistic regression. International Research Journal of Advanced Engineering and Science, 8(4), 128–131.

Blg, A. A., Athaba, M., Aboujlam, S., & Alasoud, A. M. (2020). A comparison between K-means and tree algorithm in R support vector of data mining classification problems. Journal of Applied Science, 33(2), 10.