INCISIONAL HERNIA PREDICTION USING MACHINE LEARNING MODELS
Published: 22 July 2024| Version 1 | DOI: 10.17632/s5yws2pjkn.1
Contributors:
Edgard Lozada, , , Description
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.
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Institutions
Consejo Nacional de Ciencia y Tecnologia, Centro de Investigacion e Innovacion en Tecnologias de la Informacion y Comunicacion, Centro de Investigacion en Ciencias de Informacion Geoespacial Ciudad de Mexico, Centro de Investigaciones Opticas, Hospital Regional de Alta Especialidad del Bajio
Categories
Surgery, Artificial Intelligence, Machine Learning, Hernia