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Development of prediction models of spontaneous ureteral stone passage through machine learning: Comparison with conventional statistical analysis

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Listed:
  • Jee Soo Park
  • Dong Wook Kim
  • Dongu Lee
  • Taeju Lee
  • Kyo Chul Koo
  • Woong Kyu Han
  • Byung Ha Chung
  • Kwang Suk Lee

Abstract

Objectives: To develop a prediction model of spontaneous ureteral stone passage (SSP) using machine learning and logistic regression and compare the performance of the two models. Indications for management of ureteral stones are unclear, and the clinician determines whether to wait for SSP or perform active treatment, especially in well-controlled patients, to avoid unwanted complications. Therefore, suggesting the possibility of SSP would help make a clinical decision regarding ureteral stones. Methods: Patients diagnosed with unilateral ureteral stones at our emergency department between August 2014 and September 2018 were included and underwent non-contrast-enhanced computed tomography 4 weeks from the first stone episode. Predictors of SSP were applied to build and validate the prediction model using multilayer perceptron (MLP) with the Keras framework. Results: Of 833 patients, SSP was observed in 606 (72.7%). SSP rates were 68.2% and 75.6% for stone sizes 5–10 mm and

Suggested Citation

  • Jee Soo Park & Dong Wook Kim & Dongu Lee & Taeju Lee & Kyo Chul Koo & Woong Kyu Han & Byung Ha Chung & Kwang Suk Lee, 2021. "Development of prediction models of spontaneous ureteral stone passage through machine learning: Comparison with conventional statistical analysis," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-12, December.
    • Handle: RePEc:plo:pone00:0260517
    DOI: 10.1371/journal.pone.0260517
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