Patient-Centric Skill Evaluation in IoT-Based Surgical Training with Naive Bayes Algorithm for Personalized Feedback

Abstract

Internet of Things (IoT) technology has established essential in advancing surgical training, enabling more individualized and patient-centric methods. This paper proposes a unique framework for skill assessment in surgical training, concentrating on individualized feedback using the Naive Bayes algorithm. With the use of IoT technology, surgical simulators can now collect real-time data on trainee performance while also communicating patient-centric factors into account. The data is analyzed using the suggested Naive Bayes Algorithm, which places an emphasis on providing personalized feedback to individuals. The objective of this method is to improve surgical outcomes while simultaneously increasing surgeons’ technical competence. The trainee’s abilities are evaluated in a variety of ways by the algorithm, which takes into account accuracy, recall, and other metrics. It provides to the continuing attempts to establish more efficient and individualized training procedures using IoT-based surgical training combined with machine learning approaches. These results demonstrate the promise of the Naive Bayes algorithm for improving surgical outcomes via the delivery of actionable, personalized feedback to surgeons.

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