Investigation of the visual attention role in clinical bioethics decision-making using machine learning algorithms

作     者：Daniel L. Fernandes Rodrigo Siqueira-Batista Andréia P. Gomes Camila R. Souza Israel T. da Costa Felippe da S.L. Cardoso João V. de Assis Gustavo H.L. Caetano Fabio R. Cerqueira 

作者机构：Graduate Program in Computer Science Universidade Federal de Viçosa Minas Gerais Brazil Department of Medicine and Nursing Universidade Federal de Viçosa Minas Gerais Brazil Department of Physical Education Universidade Federal de Viçosa Minas Gerais Brazil Department of Production Engineering Universidade Federal Fluminense Rio de Janeiro Brazil 

出 版 物：《Procedia Computer Science》 (计算机科学会议集)

年 卷 期：2017年第108卷

页      面：1165-1174页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Visual attention Decision-making in bioethics Mobile eye tracking Machine learning in medicine 

摘      要：This study proposes the use of a computational approach based on machine learning (ML) algorithms to build predictive models using eye tracking data. Our intention is to provide results that may support the study of medical investigation in the decision-making process in clinical bioethics, particularly in this work, in cases of euthanasia. The data used in the approach were collected from 75 students of the nursing undergraduate course using an eye tracker. The available data were processed through feature selection methods, and were later used to create models capable of predicting the euthanasia decision through ML algorithms. Statistical experiments showed that the predictive model resultant from the multilayer perceptron (MLP) algorithm led to the best performance compared with the other tested algorithms, presenting an accuracy of 90.7% and a mean area under the ROC curve of 0.90. Interesting knowledge (patterns and rules) for the studied bioethical decision-making was extracted using simulations with MLP models and inspecting the obtained decision-tree rules. The good performance shown by the obtained MLP predictive model demonstrates that the proposed investigation approach may be used to test scientific hypotheses related to visual attention and decision-making.