Predicting Spine Surgery Complications Using Machine Learning

Mohamad Hoda, Abdulmotaleb El-Saddik, Eugene Wai, Philippe Phan

Abstract: Predicting surgical complications can improve shared decision making by surgeons and patients. Recently, the use of machine learning algorithms for predicting complications has gained much attention. In this study, we used the American college of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database to compare the performance of five machine learning algorithms for predicting complications during spine surgery. The database included 173449 patients who underwent spine surgery. To thoroughly evaluate and compare the proposed machine learning algorithms, the dataset was balanced and the algorithms were applied on both the balanced and imbalanced dataset. The results indicated that no significant difference was found between the AUCs for machine learning models of the imbalanced and balanced dataset. However, when the f1 score was considered as a metric, the performance of the machine learning models trained with the balanced dataset had significantly outperformed those algorithms trained with the imbalanced dataset.