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

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.