The Use of Artificial Neural Network to Predict Surgical Outcomes After Inguinal Hernia Repair

Jeff Gao, Pavel Zagadailov, Aziz M. Merchant

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Background: Inguinal hernia repair is one of the most commonly performed surgical procedures. We developed and validated an artificial neural network (ANN) model for the prediction of surgical outcomes and the analysis of risk factors for inguinal hernia repair. Materials and methods: The American College of Surgeons National Surgical Quality Improvement Program was used to find patients who underwent inguinal hernia repair. Using logistic regression and ANN models, we evaluated morbidity, readmission, and mortality using the area under the receiver operating characteristic curves, true-positive rate, true-negative rate, false-positive rate, and false-negative rates. Results: There was no significant difference in the power of the ANN and logistic regression for predicting mortality, readmission, and all morbidities after inguinal hernia repair. Risk factors for morbidity, readmission, and mortality outcomes identified using ANN were consistent with logistic regression analysis. Conclusions: ANNs perform comparably to logistic regression models in the prediction of outcomes after inguinal hernia repair. ANNs may be a useful tool in risk factor analysis of hernia surgery and clinical applications.

Original languageEnglish (US)
Pages (from-to)372-378
Number of pages7
JournalJournal of Surgical Research
StatePublished - Mar 2021

All Science Journal Classification (ASJC) codes

  • Surgery


  • Artificial neural network
  • Inguinal hernia repair
  • Predictive modeling
  • Surgical outcomes


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