An Enhanced Optimization-Based Support Vector Machine for Urolithiasis Prediction with Topological Guidance and Reflective Learning

Yuanzhe Ding; Ao Ma; Zijian Chen; Tianyue Liu; Yun Jin; Weiqiang Ning; Kaijie Xu; Junyi Lu; Huiling Chen; Huiliang Wang; Wei Chen

doi:doi:10.11648/j.acm.20251406.13

Research Article |

| Peer-Reviewed

An Enhanced Optimization-Based Support Vector Machine for Urolithiasis Prediction with Topological Guidance and Reflective Learning

Yuanzhe Ding

, Ao Ma

, Zijian Chen

, Tianyue Liu

, Yun Jin

, Weiqiang Ning

, Kaijie Xu

, Junyi Lu

, Huiling Chen

, Huiliang Wang

, Wei Chen^*

Published in Applied and Computational Mathematics (Volume 14, Issue 6)

Received: 10 September 2025 Accepted: 30 September 2025 Published: 3 December 2025

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Abstract

Urolithiasis, a condition characterized by the formation of stones in the urinary tract, is influenced by a confluence of factors, including genetic predispositions, dietary habits, and inadequate hydration. This condition can lead to urinary obstruction and pain, elevate the risk of infections, and, in severe cases, potentially impair kidney function. Early identification and prediction are crucial for preventing the formation of urinary stones and mitigating their consequent impacts. In this study, a machine learning model, named bTRWOA-SVM, was developed utilizing data from 1027 suspected patients at the First Affiliated Hospital of Wenzhou Medical University. This model synergizes the whale optimization algorithm (WOA) with the support vector machine (SVM) and introduces enhancements through the triangular topological search strategy and reflective learning operator to augment the search proficiency of the WOA, resulting in a variant termed TRWOA. Comparative analysis against a range of contemporaries using the CEC 2017 benchmark suite substantiated TRWOA's effective optimization capabilities and convergence precision. Furthermore, the constructed bTRWOA-SVM model, when applied to clinical data about urolithiasis, achieved a predictive accuracy of 98.830% and a specificity of 97.665%. Conclusively, the model also identified critical features influencing urolithiasis prediction, including urinary bilirubin, total protein, pH value, creatinine, and direct bilirubin, thereby providing a scientific basis for the early diagnosis and treatment of urolithiasis.

Published in	Applied and Computational Mathematics (Volume 14, Issue 6)
DOI	10.11648/j.acm.20251406.13
Page(s)	323-348
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Urolithiasis, Machine Learning, Support Vector Machine, Whale Optimization Algorithm, Predictive Modeling, Feature Selection

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Ding, Y., Ma, A., Chen, Z., Liu, T., Jin, Y., et al. (2025). An Enhanced Optimization-Based Support Vector Machine for Urolithiasis Prediction with Topological Guidance and Reflective Learning. Applied and Computational Mathematics, 14(6), 323-348. https://doi.org/10.11648/j.acm.20251406.13

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Ding, Y.; Ma, A.; Chen, Z.; Liu, T.; Jin, Y., et al. An Enhanced Optimization-Based Support Vector Machine for Urolithiasis Prediction with Topological Guidance and Reflective Learning. Appl. Comput. Math. 2025, 14(6), 323-348. doi: 10.11648/j.acm.20251406.13

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Ding Y, Ma A, Chen Z, Liu T, Jin Y, et al. An Enhanced Optimization-Based Support Vector Machine for Urolithiasis Prediction with Topological Guidance and Reflective Learning. Appl Comput Math. 2025;14(6):323-348. doi: 10.11648/j.acm.20251406.13

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@article{10.11648/j.acm.20251406.13,
  author = {Yuanzhe Ding and Ao Ma and Zijian Chen and Tianyue Liu and Yun Jin and Weiqiang Ning and Kaijie Xu and Junyi Lu and Huiling Chen and Huiliang Wang and Wei Chen},
  title = {An Enhanced Optimization-Based Support Vector Machine for Urolithiasis Prediction with Topological Guidance and Reflective Learning
},
  journal = {Applied and Computational Mathematics},
  volume = {14},
  number = {6},
  pages = {323-348},
  doi = {10.11648/j.acm.20251406.13},
  url = {https://doi.org/10.11648/j.acm.20251406.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20251406.13},
  abstract = {Urolithiasis, a condition characterized by the formation of stones in the urinary tract, is influenced by a confluence of factors, including genetic predispositions, dietary habits, and inadequate hydration. This condition can lead to urinary obstruction and pain, elevate the risk of infections, and, in severe cases, potentially impair kidney function. Early identification and prediction are crucial for preventing the formation of urinary stones and mitigating their consequent impacts. In this study, a machine learning model, named bTRWOA-SVM, was developed utilizing data from 1027 suspected patients at the First Affiliated Hospital of Wenzhou Medical University. This model synergizes the whale optimization algorithm (WOA) with the support vector machine (SVM) and introduces enhancements through the triangular topological search strategy and reflective learning operator to augment the search proficiency of the WOA, resulting in a variant termed TRWOA. Comparative analysis against a range of contemporaries using the CEC 2017 benchmark suite substantiated TRWOA's effective optimization capabilities and convergence precision. Furthermore, the constructed bTRWOA-SVM model, when applied to clinical data about urolithiasis, achieved a predictive accuracy of 98.830% and a specificity of 97.665%. Conclusively, the model also identified critical features influencing urolithiasis prediction, including urinary bilirubin, total protein, pH value, creatinine, and direct bilirubin, thereby providing a scientific basis for the early diagnosis and treatment of urolithiasis.
},
 year = {2025}
}

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TY  - JOUR
T1  - An Enhanced Optimization-Based Support Vector Machine for Urolithiasis Prediction with Topological Guidance and Reflective Learning

AU  - Yuanzhe Ding
AU  - Ao Ma
AU  - Zijian Chen
AU  - Tianyue Liu
AU  - Yun Jin
AU  - Weiqiang Ning
AU  - Kaijie Xu
AU  - Junyi Lu
AU  - Huiling Chen
AU  - Huiliang Wang
AU  - Wei Chen
Y1  - 2025/12/03
PY  - 2025
N1  - https://doi.org/10.11648/j.acm.20251406.13
DO  - 10.11648/j.acm.20251406.13
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 323
EP  - 348
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20251406.13
AB  - Urolithiasis, a condition characterized by the formation of stones in the urinary tract, is influenced by a confluence of factors, including genetic predispositions, dietary habits, and inadequate hydration. This condition can lead to urinary obstruction and pain, elevate the risk of infections, and, in severe cases, potentially impair kidney function. Early identification and prediction are crucial for preventing the formation of urinary stones and mitigating their consequent impacts. In this study, a machine learning model, named bTRWOA-SVM, was developed utilizing data from 1027 suspected patients at the First Affiliated Hospital of Wenzhou Medical University. This model synergizes the whale optimization algorithm (WOA) with the support vector machine (SVM) and introduces enhancements through the triangular topological search strategy and reflective learning operator to augment the search proficiency of the WOA, resulting in a variant termed TRWOA. Comparative analysis against a range of contemporaries using the CEC 2017 benchmark suite substantiated TRWOA's effective optimization capabilities and convergence precision. Furthermore, the constructed bTRWOA-SVM model, when applied to clinical data about urolithiasis, achieved a predictive accuracy of 98.830% and a specificity of 97.665%. Conclusively, the model also identified critical features influencing urolithiasis prediction, including urinary bilirubin, total protein, pH value, creatinine, and direct bilirubin, thereby providing a scientific basis for the early diagnosis and treatment of urolithiasis.

VL  - 14
IS  - 6
ER  -

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Author Information

Yuanzhe Ding

The First Clinical Medical College, Wenzhou Medical University, Wenzhou, PR China; Department of Urology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China

http://orcid.org/0009-0003-7387-1835
Ao Ma

The First Clinical Medical College, Wenzhou Medical University, Wenzhou, PR China; Department of Urology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China

http://orcid.org/0009-0001-9014-0327
Zijian Chen

The First Clinical Medical College, Wenzhou Medical University, Wenzhou, PR China; Department of Urology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China

http://orcid.org/0009-0001-7798-7929
Tianyue Liu

The First Clinical Medical College, Wenzhou Medical University, Wenzhou, PR China; Department of Urology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China

http://orcid.org/0009-0000-6823-0783
Yun Jin

The First Clinical Medical College, Wenzhou Medical University, Wenzhou, PR China; Department of Urology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China

http://orcid.org/0009-0001-9522-3113
Weiqiang Ning

The Third Clinical Medical College, Wenzhou Medical University, Wenzhou, PR China; People's Hospital of Wenzhou, Wenzhou, PR China

http://orcid.org/0009-0006-9287-5008
Kaijie Xu

The Third Clinical Medical College, Wenzhou Medical University, Wenzhou, PR China; People's Hospital of Jinhua, Jinhua, PR China

http://orcid.org/0000-0001-8259-7223
Junyi Lu

The Third Clinical Medical College, Wenzhou Medical University, Wenzhou, PR China; People's Hospital of Jinhua, Jinhua, PR China

http://orcid.org/0009-0002-4220-0513
Huiling Chen

College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, PR China

http://orcid.org/0000-0002-7714-9693
Huiliang Wang

Department of Urology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China

http://orcid.org/0009-0005-0317-8263
Wei Chen

The First Clinical Medical College, Wenzhou Medical University, Wenzhou, PR China; Department of Urology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China

Contact Email

http://orcid.org/0009-0003-9822-7683

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APA Style

Ding, Y., Ma, A., Chen, Z., Liu, T., Jin, Y., et al. (2025). An Enhanced Optimization-Based Support Vector Machine for Urolithiasis Prediction with Topological Guidance and Reflective Learning. Applied and Computational Mathematics, 14(6), 323-348. https://doi.org/10.11648/j.acm.20251406.13

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ACS Style

Ding, Y.; Ma, A.; Chen, Z.; Liu, T.; Jin, Y., et al. An Enhanced Optimization-Based Support Vector Machine for Urolithiasis Prediction with Topological Guidance and Reflective Learning. Appl. Comput. Math. 2025, 14(6), 323-348. doi: 10.11648/j.acm.20251406.13

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AMA Style

Ding Y, Ma A, Chen Z, Liu T, Jin Y, et al. An Enhanced Optimization-Based Support Vector Machine for Urolithiasis Prediction with Topological Guidance and Reflective Learning. Appl Comput Math. 2025;14(6):323-348. doi: 10.11648/j.acm.20251406.13

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@article{10.11648/j.acm.20251406.13,
  author = {Yuanzhe Ding and Ao Ma and Zijian Chen and Tianyue Liu and Yun Jin and Weiqiang Ning and Kaijie Xu and Junyi Lu and Huiling Chen and Huiliang Wang and Wei Chen},
  title = {An Enhanced Optimization-Based Support Vector Machine for Urolithiasis Prediction with Topological Guidance and Reflective Learning
},
  journal = {Applied and Computational Mathematics},
  volume = {14},
  number = {6},
  pages = {323-348},
  doi = {10.11648/j.acm.20251406.13},
  url = {https://doi.org/10.11648/j.acm.20251406.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20251406.13},
  abstract = {Urolithiasis, a condition characterized by the formation of stones in the urinary tract, is influenced by a confluence of factors, including genetic predispositions, dietary habits, and inadequate hydration. This condition can lead to urinary obstruction and pain, elevate the risk of infections, and, in severe cases, potentially impair kidney function. Early identification and prediction are crucial for preventing the formation of urinary stones and mitigating their consequent impacts. In this study, a machine learning model, named bTRWOA-SVM, was developed utilizing data from 1027 suspected patients at the First Affiliated Hospital of Wenzhou Medical University. This model synergizes the whale optimization algorithm (WOA) with the support vector machine (SVM) and introduces enhancements through the triangular topological search strategy and reflective learning operator to augment the search proficiency of the WOA, resulting in a variant termed TRWOA. Comparative analysis against a range of contemporaries using the CEC 2017 benchmark suite substantiated TRWOA's effective optimization capabilities and convergence precision. Furthermore, the constructed bTRWOA-SVM model, when applied to clinical data about urolithiasis, achieved a predictive accuracy of 98.830% and a specificity of 97.665%. Conclusively, the model also identified critical features influencing urolithiasis prediction, including urinary bilirubin, total protein, pH value, creatinine, and direct bilirubin, thereby providing a scientific basis for the early diagnosis and treatment of urolithiasis.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - An Enhanced Optimization-Based Support Vector Machine for Urolithiasis Prediction with Topological Guidance and Reflective Learning

AU  - Yuanzhe Ding
AU  - Ao Ma
AU  - Zijian Chen
AU  - Tianyue Liu
AU  - Yun Jin
AU  - Weiqiang Ning
AU  - Kaijie Xu
AU  - Junyi Lu
AU  - Huiling Chen
AU  - Huiliang Wang
AU  - Wei Chen
Y1  - 2025/12/03
PY  - 2025
N1  - https://doi.org/10.11648/j.acm.20251406.13
DO  - 10.11648/j.acm.20251406.13
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 323
EP  - 348
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20251406.13
AB  - Urolithiasis, a condition characterized by the formation of stones in the urinary tract, is influenced by a confluence of factors, including genetic predispositions, dietary habits, and inadequate hydration. This condition can lead to urinary obstruction and pain, elevate the risk of infections, and, in severe cases, potentially impair kidney function. Early identification and prediction are crucial for preventing the formation of urinary stones and mitigating their consequent impacts. In this study, a machine learning model, named bTRWOA-SVM, was developed utilizing data from 1027 suspected patients at the First Affiliated Hospital of Wenzhou Medical University. This model synergizes the whale optimization algorithm (WOA) with the support vector machine (SVM) and introduces enhancements through the triangular topological search strategy and reflective learning operator to augment the search proficiency of the WOA, resulting in a variant termed TRWOA. Comparative analysis against a range of contemporaries using the CEC 2017 benchmark suite substantiated TRWOA's effective optimization capabilities and convergence precision. Furthermore, the constructed bTRWOA-SVM model, when applied to clinical data about urolithiasis, achieved a predictive accuracy of 98.830% and a specificity of 97.665%. Conclusively, the model also identified critical features influencing urolithiasis prediction, including urinary bilirubin, total protein, pH value, creatinine, and direct bilirubin, thereby providing a scientific basis for the early diagnosis and treatment of urolithiasis.

VL  - 14
IS  - 6
ER  -

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