Emotion Analysis for Online Patient Care using Machine Learning

Abstract

Nowadays, telemedicine has become increasingly important for patient monitoring, as due to the confinement caused by the pandemic, public care has been banned by the government in many countries. For this reason, online patient care has been exercised by many physicians. Therefore, this research paper makes a proposal of emotion analysis for online patient care using machine learning, a branch of artificial intelligence that has been very successful in different studies. In this article, a methodology capable of identifying the emotional state in which the patient is and that is able to classify it at the time of being in an online care was applied. In short, the Dlib library containing algorithms in Phyton language was used for data collection. For data collection, a tool was used to identify coordinates of the facial features of the study. Finally, a model was applied using the SVM, vector machine for the classification and detection of evidence extracted from emotions. As a result, the mood of the patients is evaluated and the necessary measures are taken for better online care.