Background: Kidney and ureter stones are the third pathologies in urological diseases. Less invasive treatments such as transureteral lithotripsy and extracorporeal shock wave lithotripsy are used to treat ureteral stones. Data mining has provided the possibility of improving decision-making in choosing the optimal treatment. In this paper predictive models for the detection of ureter stone treatment (first model) and its outcome (second model) is developed based on the patient's demographic, clinical, and laboratory factors.
Methods and material: In this cross-sectional study a questionnaire was used to identify the most effective features in the predictive models, and Information on 440 patients was collected. The models were constructed using machine learning techniques (Multilayer perceptron, Classification, and regression tree, k-nearest neighbors, Support vector machine, Naïve Bayes classifier, Random Forest, and AdaBoost) in the Bigpro1 analytical system.
Results: Among the Holdout and K-fold cross-validation methods used, the Holdout method showed better performance. From the data-based balancing methods used in the second model, the Synthetic Minority oversampling technique showed better performance. Also, the AdaBoost algorithm had the best performance. In this algorithm, accuracy, sensitivity, specificity, precision, F- measure, and Area under the carve in the first model were 89%, 87%, 91%, 90%, 89%, and 94% respectively, and in the second model were 81%, 81%, 82%, 84%, 82%, and 85% respectively.
Conclusions: The results were promising and showed that the data mining techniques could be a powerful assistant for urologists to predict a surgical outcome and also to choose an appropriate surgical treatment for removing ureter stones.
Keywords: Data mining; extracorporeal shockwave lithotripsy; lithotripsy; machine learning; ureteral calculi/therapy; ureteroscopic surgeries.
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