A machine learning model for predicting surgical intervention in renal colic due to ureteral stone(s) 5 mm

A 75–89% expulsion rate is reported for ureteric stones ≤ 5 mm. We explored which parameters predict justified surgical intervention in cases of pain caused by p p p p = 0.007) compared to those who had no surgical intervention. The model accuracy was 0.8. Larger stone size and proximal location were the most important features in predicting the need for intervention. Altogether with pulse and ER visits, they contributed 73% of the final prediction for each patient. Although a high expulsion rate is expected for ureteral stones