XGBoost for Predict Incisional Hernia

The objective of this study is to develop a predictive model of IH based on machine learning with the use of the XGBoost technique, this will help surgeons in charge of abdominal wall closure to have objective support to determine high-risk patients and in them modify the closure technique or use a mesh according to their choice or the degree of contamination of the abdominal cavity.

Inclusion Criteria:Patients older than 18 years of agePostoperative midline exploratory laparotomy, who underwent urgent or scheduled surgery, regardless of their underlying diagnosis,included between January 2010 and December 2016 and who completed 24 months of follow-up after surgery initial surgery.Exclusion Criteria:Reoperated for any cuestion diferent to present of herniaManagement of open abdomen