학술논문
Prediction of infective complications after retrograde intra renal surgery using Machine learning.
Document Type
Article
Author
Source
Subject
*INFECTION risk factors
*KIDNEY stones
*MULTIVARIATE analysis
*MACHINE learning
*RETROSPECTIVE studies
*RISK assessment
*COMPARATIVE studies
*SEPSIS
*DESCRIPTIVE statistics
*UROLOGICAL surgery
*RECEIVER operating characteristic curves
*SENSITIVITY & specificity (Statistics)
*LOGISTIC regression analysis
*PROPORTIONAL hazards models
*LONGITUDINAL method
*DISEASE risk factors
SURGICAL complication risk factors
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Language
ISSN
1364-5706
Abstract
To compare the models obtained with classical statistical methods and machine learning (ML) algorithms to predict postoperative infective complications (PICs) after retrograde intrarenal surgery (RIRS). Patients who underwent RIRS between January 2014 and December 2020 were retrospectively screened. Patients who did not develop PICs were classified as Group 1 and patients who developed as Group 2. Three-hundred and twenty-two patients were included in the study; 279 patients (86.6%) who did not develop PICs were classified as Group 1, and 43 patients (13.3%) who developed PICs were classified as Group 2. In multivariate analysis, the presence of diabetes mellitus, preoperative nephrostomy, and stone density were determined to be factors that significantly predicted the development of PICs. The area under the curve (AUC) of the model obtained by classical Cox regression analysis was 0.785, and the sensitivity and specificity were 74% and 67%, respectively. With the Random Forest, K- Nearest Neighbour, and Logistic Regression methods, the AUC was calculated as 0.956, 0.903, and 0.849, respectively. RF's sensitivity and specificity were calculated as 87% and 92%, respectively. With ML, more reliable and predictive models can be created than with classical statistical methods. [ABSTRACT FROM AUTHOR]