Computational Analysis and Classification of Hernia Repairs

Abstract: Problems related to ventral hernia repairs (VHR) are very common, and evaluating themusing computational methods can assist in selecting the most appropriate treatment. This studyis based upon data from 3339 patients from different European countries observed during the last12 years (2012–2023), which were collected by specialists in hernia surgery. Most patients underwentstandard surgical procedures, with a growing trend towards laparoscopic surgery. This paperfocuses on statistically evaluating the treatment methods in relation to patient age, body mass index(BMI), and the type of repair. Appropriate mathematical methods are employed to extract andclassify the selected features, with emphasis on computational and machine-learning techniques. Thepaper presents surgical hernia treatment statistics related to patient age, BMI, and repair methods.The main conclusions point to mean groin hernia repair (GHR) complications of 19% for patientsin the database. The accuracy of separating GHR mesh surgery with and without postoperativecomplications reached 74.4% using a two-layer neural network classification. Robotic surgeriesrepresent 22.9% of all the evaluated hernia repairs. The proposed methodology suggests bothan interdisciplinary approach and the utilization of computational intelligence in hernia surgery,potentially applicable in a clinical setting.