INCISIONAL HERNIA PREDICTION USING MACHINE LEARNING MODELS

Retrospective cohort study type of diagnostic tests. Patients over 18 years of age who underwent midline laparotomy were included. The main outcome was the occurrence of IH. Three ML methods were evaluated: Logistic Regression, Decision Tree, and XGBoost. Each model's predictive capacity, Friedman range score, and clinical utility were assessed. The usefulness of all models was further evaluated using Bayes' theorem to determine the change in prevalence after applying the models.