Efficacy of machine learning algorithms versus conventional assessment techniques in predicting postoperative complications in general surgery: a comprehensive literature review

Abstract

We examine machine learning algorithms' efficacy and core abilities versus conventional methods in predicting postoperative complications in general surgery. Our findings revealed that machine learning algorithms generally supervised and non-supervised assessment techniques in predicting postoperative complications, offering greater accuracy and reliability, thus suggesting a shift towards integrating these advanced tools in clinical practice. This paper discusses the potential of machine learning to revolutionize postoperative care, enhancing prediction accuracy and improving patient outcomes significantly.