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.

Retrospective and observational study. The predictions will make using machine learning models. The programs use the scikit-learn, xgboost and catboost Python packages for modeling.

The evaluation of models will be using fourfold cross-validation, the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics calculated on the union of the test sets of the cross-validation.

The most critical factors and their contribution to the prediction will identify using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP).

Inclusion Criteria:

• Patients older than 18 years of age
• Postoperative 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 hernia
• Management of open abdomen

Patients older than 18 years of age, postoperative midline exploratory laparotomy, who underwent urgent or scheduled surgery, regardless of their underlying diagnosis, were included between January 2010 and December 2016 and who completed 24 months of follow-up after surgery initial surgery.

Once the database to be analyzed is identified, the presence of other researchers will be verified to certify the veracity of the data.