Optimizing Surgical Techniques for Ventral Hernias: A Comparative Analysis Using Machine Learning Models

Ventral hernias are common surgical conditions that can be treated with different optimal surgical techniques. The purpose of this study is to perform analysis of patient data who suffered with ventral Hernia. The best surgical procedure for ventral hernias is determined through comparing the outcome of open mesh versus laparoscopic surgery. During pre-processing, the dataset is augmented to 200 records for better model training and reduced overfitting. The use of machine learning (ML) and deep learning algorithms to predict the optimal surgical procedure based on various factors and complications is examined in this study. Several machine learning and deep learning techniques, such as decision trees, random forests, logistic regression, support vector machines (SVM), and artificial neural networks (ANNs), have been studied in this study. Random Forest Classifier, with an accuracy rate of 95.00%, is the best approach out of the rest. This study emphasizes the importance of machine learning in developing predictive models that can assist surgeons in selecting the most appropriate surgical approach for ventral hernias.