Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models?

ABSTRACT:

Objective

To externally validate by revision and update the study on the efficacy of nosocomial infection control (SENIC) model of surgical site infection (SSI) using logistic regression (LR) and machine learning (ML) approaches.

Methods

A retrospective analysis of hospital database-derived data from patients that had undergone gastrointestinal, colorectal and hernia surgeries (identified by ICD-9-CM). The SENIC index was calculated and fitted in an LR. MLs were developed using decision-tree (DT), random forest (RF), extreme-gradient-boosting (XGBoost) and Naïve Bayes (NB).

Results

The prevalence of an SSI was 3.21% (404 of 12 596 surgeries; 95% confidence interval [CI] 2.91%, 3.53%). The C-statistic for the original SENIC model was 0.668 (95% CI 0.648, 0.688) with an

SUBMITTER: Piebpien P 

PROVIDER: S-EPMC11571240 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature