Enhancing Neonatal Surgical Outcomes with Machine Learning for Predictive Analysis

Abstract

The delicate nature of neonates and the high risk of postoperative complications make neonatal surgery particularly challenging. This study examines the use of machine learning (ML) techniques to predict surgical outcomes and enhance decision-making in neonatal surgery. Using a dataset of neonatal surgical cases gathered from multiple hospitals, we implemented and compared several ML algorithms, including Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Deep Neural Networks (DNN). Key features, including preoperative vital signs, laboratory results, gestational age, and surgical parameters, were used to train the models, and performance metrics, including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic (ROC-AUC) were assessed to identify the most effective model. Our findings show that, with an AUC of 0.92, the DNN model performed better than both conventional ML techniques and traditional statistical methods, as opposed to 0.85 for RF, 0.81 for SVM, and 0.78 for LR. These findings imply that ML-based predictive modeling can provide useful insights for neonatal surgeons, helping to optimize surgical planning and after care. Future work will focus on real-time clinical deployment and integration with electronic health records (EHR) to provide automated decision assistance.