Machine Learning-Based Prediction of Urolithiasis Recurrence Using Patient’s Clinical Data, Demography, and CT Findings
Urology Journal,
Vol. 22 No. 06 (2025),
11 January 2026
,
Page 289-300
https://doi.org/10.22037/uj.v22i.8544
Abstract
Purpose: Urolithiasis is the condition of forming stones inside urinary tract with diverse shape, size, and location. The sooner urolithiasis is diagnosed, the easier it is to treat and prevent complication. This study aims to propose a method for predicting urolithiasis recurrence based on machine learning methods.
Materials and Methods: The proposed method uses clinical data, demographics, and CT findings of 4246 patients who were referred to the clinic once or multiple times within three years. The proposed method has three main phases of data engineering and pre-processing, machine learning prediction model development, and performance evaluation. In addition, the performance of six machine learning-based classifiers is evaluated by performance metric calculation, ROC curve analysis, calibration analysis, and decision curve analysis.
Results: The results of 10 independent repeats of the proposed method using a train/test split evaluation strategy reveal that the best-performing classifier is random forest with the area under the ROC curve, sensitivity, and positive predictive value of 0.64, 0.87, and 0.84, respectively. On the other hand, k-fold cross-validation: A comma is needed after "hand" and before "k-fold" evaluation strategy reveals that the best-performing classifier again is RF, with the area under the ROC curve, sensitivity, and positive predictive value of 0.63, 0.90, and 0.83, respectively. Moreover, the brier score of 0.18 shows that this classifier is well-calibrated among other evaluated classifiers.
Conclusion: This study presents a practical application of predictive machine learning methods for predicting urolithiasis recurrence with clinically acceptable accuracy compared to traditional scoring systems. To select the best classifier, six different predictive ML models have been evaluated using different performance metrics and analysis tools.
- Artificial Intelligence
- Machine Learning
- Urolithiasis
- Recurrence Prediction
How to Cite
References
1. Kachkoul R, Touimi GB, El Mouhri G, El Habbani R, Mohim M, Lahrichi A. Urolithiasis: history, epidemiology, aetiologic factors and management. Malays J Pathol. 2023;45:333-52.
2. Banov P, Ceban E. The efficacy of metaphylaxis in treatment of recurrent urolithiasis. J Med Life. 2017;10:188-93.
3. Corbo J, Wang J. Kidney and ureteral stones. Emerg Med Clin North Am. 2019;37:637-48.
4. Baowaidan F, Zugail AS, Lyoubi Y, et al. Incidence and risk factors for urolithiasis recurrence after endourological management of kidney stones: a retrospective single-centre study. Prog Urol. 2022;32:601-7.
5. Chen YH, Li WM, Juan YS, Huang TY, Wang YC, Lee HY. A comparison of S.T.O.N.E nephrolithometry scoring system, Guy’s stone score, and Seoul National University Renal Stone Complexity (S-ReSC) in predicting mini-PCNL stone-free rate. Urolithiasis. 2024;52:1-8.
6. Hameed BMZ, Dhavileswarapu AVL S, Raza SZ, et al. Artificial intelligence and its impact on urological diseases and management: a comprehensive review of the literature. J Clin Med. 2021;10:1864.
7. Shah M, Naik N, Somani BK, Hameed BZ. Artificial intelligence (AI) in urology—current use and future directions: an iTRUE study. Turk J Urol. 2020;46:27-39.
8. Homayoun H, Saligheh Rad H, Abbasian Ardakani A. The role of artificial intelligence in urology practice. Transl Res Urol. 2022;4:1-3.
9. Suarez-Ibarrola R, Hein S, Reis G, Gratzke C, Miernik A. Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer. World J Urol. 2020;38:2329-47.
10. Soleimani Neysiani B, Homayoun H. Medical text and image processing: applications, methods, issues, and challenges. In: Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications. 1st ed. CRC Press; 2022.
11. Mitchell T. Machine Learning. McGraw Hill; 1997.
12. Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, et al. Missing value estimation methods for DNA microarrays. Bioinformatics. 2001;17:520-5.
13. Theodoridis S, Koutroumbas K. Pattern Recognition. Elsevier; 2009.
14. He H, Bai Y, Garcia EA, Li S. ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks. IEEE; 2008:1322-8.
15. Zhang H. The optimality of naive Bayes. AAAI. 2004.
16. Bennett KP, Campbell C. Support vector machines. ACM SIGKDD Explor Newsl. 2000;2:1-13.
17. Breiman L. Random forests. Mach Learn. 2001;45:5-32.
18. Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29:1189-232.
19. Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inf Theory. 1967;13:21-7.
20. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323.
21. Belete DM, Huchaiah MD. Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results. Int J Comput Appl. 2022;44:875-6.
22. Niculescu-Mizil A, Caruana R. Predicting good probabilities with supervised learning. In: Proceedings of the 22nd International Conference on Machine Learning. 2005:625-32.
23. Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27:861-74.
24. Austin PC, Harrell FE, van Klaveren D. Graphical calibration curves and the integrated calibration index (ICI) for survival models. Stat Med. 2020;39:2714-42.
25. Vickers AJ, van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis. Diagn Progn Res. 2019;3:18.
26. Ali J, Khan R, Ahmad N, Maqsood I. Random forests and decision trees. Int J Comput Sci Issues. 2012;9.
27. Gonzalez R, Saha A, Campbell CJV, Nejat P, Lokker C, Norgan AP. Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: challenges and opportunities. J Pathol Inform. 2024;15:100347.
28. Feng J, Yu Y, Zhou ZH. Multi-layered gradient boosting decision trees. Available from: www.kaggle.com
29. Doyle P, Gong W, Hsi R, Kavoussi N. Machine learning models to predict kidney stone recurrence using 24-hour urine testing and electronic health record-derived features. Res Sq. 2023.
30. Geraghty RM, Wilson I, Olinger E, Cook P, Troup S, et al. Routine urinary biochemistry does not accurately predict stone type nor recurrence in kidney stone formers: a multicentre, multimodel, externally validated machine-learning study. J Endourol. 2023;37:1295-304.
31. Shee K, Liu AW, Chan C, Yang H, Sui W, Desai M, et al. A novel machine-learning algorithm to predict stone recurrence with 24-hour urine data. J Endourol. 2024;38:809-16.
32. Rodjani A, Putra CN, Raharja PAR, et al. Effectivity and safety profile of oxalate decarboxylase in hyperoxaluria patient: a meta-analysis and systematic review. Bali Med J. 2024;13:782-9.
33. Atmoko W, Raharja PAR, Birowo P, et al. Genetic polymorphisms as prognostic factors for recurrent kidney stones: a systematic review and meta-analysis. 2021;16.
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