کنفرانس مهندسی برق ایران

صفحه اصلی / سی و یکمین کنفرانس بین المللی مهندسی برق

A modified Dempster Shafer approach to classification in surgical skill assessment

نویسندگان :

Arash Iranfar¹ Mohammad Soleymannejad² Behzad Moshiri³ Hamid D. Taghirad⁴

1- دانشگاه تهران 2- دانشگاه تهران 3- دانشگاه تهران 4- دانشگاه صنعتی خواجه نصیرالدین طوسی

کلمات کلیدی :

Skill Assessment،Classification،Evidence combination،Dempster-Shafer theory

چکیده :

Artificial intelligence systems are usually implemented either using machine learning or expert systems. Machine learning methods are usually more accurate and applicable to a broader range of applications. Expert systems, on the other hand, require much less data for training and generate more comprehensible results. These characteristics are typically desired in the fields of surgery and medicine because there isn’t much data available. In order to give a machine’s decisions a deeper level of semantics, it is also advantageous to incorporate a doctor’s expertise into it. Furthermore, it is safer to understand the reasoning behind a machine’s choices. In this paper, a Dempster-Shafer Theory (DST) based expert system is suggested for the task of surgical training skill assessment. An interval-based probabilistic feature analysis was applied to the data to assign values to the mass functions. Zhang’s rule of combination was applied to handle the conflicting evidence in the prediction phase. The performance of the proposed method was compared to another DST classifier, SVM, and XGBoost. Our method outperforms SVM and other DST classifiers, but it is not as precise as XGBoost. By reducing the size of the dataset, the added benefit of using an expert system as opposed to a machine learning method was explored further. The performance of the suggested method is not adversely affected by the size of the dataset, whereas the XGBoost classifier is.