# Computational Analysis and Classification of Hernia Repairs
## 4 Discussion  
Records of 3339 hernia surgeries with complete records performed during the last 12 years include 2048 GHR treatments and they indicate a higher proportion of male patients (75.4%) compared to female patients (22.9%) with mesh-based hernia repairs. While robotic surgery (performed in 22.9% of cases) can be beneficial, an open surgical approach or a combination of open and robotic approaches may be necessary for achieving optimal outcomes in cases of large or complex hernias [25]. Notably, the accuracy of distinguishing GHR mesh surgery with and without postoperative complications reached 74.4% with a true positive rate of 77.7% using a two-layer neural network classification. The problem of class imbalance was solved by the SMOTE technique. Post-surgery complications occurred in 19% of cases. Figure 5 illustrates the classification of GHR surgery outputs, presenting the comparison of GHR surgery results without and with post-surgery complications based upon two features (BMI and age). Complications after GHR are higher for patients with higher BMI and age. This study is based on present records in the database of the European Hernia Society ([[https://www.europeanherniasociety.eu/https://ehs-hernia-registry.com/](https://www.europeanherniasociety.eu/https://ehs-hernia-registry.com/)]([https://www.europeanherniasociety.eu/https://ehs-hernia-registry.com/](https://www.europeanherniasociety.eu/https://ehs-hernia-registry.com/)), (accessed on 14 February 2024)), which serves as an alternative to other databases, including Foreningen Dansk Herniedatabase [36]. Registries have proven to be valuable tools in achieving better surgical results, a lower risk of complications, and a shorter recovery time. The advantages and limitations of different hernia databases are discussed in various references [37]. Our findings are consistent with other published evidence that focuses on comparing different patient groups and operative alternatives, particularly robotic-assisted minimally invasive and open techniques [38]. However, all these studies lack patient involvement [39]. The paper presents efficient access to database records through the proposed web page, which should be modified further in the future. The use of computational methods shows how artificial intelligence is gradually transforming the approach to hernia surgery. In this field, machine learning has the potential to enhance accuracy and efficiency in several general ways:  
* Preoperative planning: Machine-learning algorithms can analyze medical images such as CT scans and MRI to create 3D models of the patient's hernia and surrounding  
Figure 9: Statistics of the type of surgery, intra-operative complications during the hernia surgery, and postoperative complications for the set of GHR patients included in the European hernia database and treated during the period of 6 years between the years of 2018 and 2023.  
tissue. This enables surgeons to plan the surgery more precisely and reduce the risk of complications. * Intra-operative assistance: During surgery, machine-learning algorithms can analyze real-time video footage from the surgical site and provide the surgeon with feedback on the location of the hernia, the depth of the incision, and the placement of surgical instruments. * Postoperative monitoring: Machine-learning algorithms can analyze patient data, such as vital signs, laboratory results, and medication records, to predict the likelihood of postoperative complications and facilitate early intervention. The present study is devoted to an analysis of GHR hernia surgeries of males/females and surgeries with/without complications for patients with different BMIs and ages. The accuracy of the classification system relies on the completeness and accuracy of the medical records used to identify the surgical procedures performed. The specificity of the classification system for groin hernia repairs is sufficiently high, as the type of surgical procedure performed is clearly defined, indicating the proportion of correctly identified individuals.