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.

本数据集来源于诊断测试领域的回顾性队列研究。研究纳入了年满18周岁且接受正中剖腹术的患者，主要结局指标为切口疝（Incisional Hernia, IH）的发生情况。本次研究评估了三种机器学习（Machine Learning, ML）方法：逻辑回归、决策树与XGBoost。对各模型的预测性能、弗里德曼范围评分及临床实用性进行了评价。此外，本研究还借助贝叶斯定理（Bayes' theorem）对所有模型的实用性展开进一步评估，以明确应用模型后患病率的变化情况。