Authors : Mi-Lim Choi, Myung Jae Lim, Young-Man Kwon and Dong-Kun Chung
Abstract: Regarding the seriousness of infection within the Emergency Room (ER), due to the various infectious diseases from virus such as Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) that occurred recently in the last few years, many patients that visited the ER for treatment were infected and resulted in death. To monitor the pollution level in the ER for preventing this occurrence of diseases in advance, the device is studied which is easy to install, counts the air particles by using the back scattering method and has IR sensor. The dangerous pollution level is alerted in advance to notify the hospital personnel, patients and guardians and the air particles within the ER are collected and performed with quick analysis to verify the pathogen and harmful virus before the contamination. To enable this, the decision tree algorithm of deep learning, a hot issue in the present and a part of machine learning is used to study the similar cases and to deliver the suspicious danger signals quickly. CHAID (Chi-squared Automatic Interaction Detection) of the decision tree has continuous target variables and stops the growth of the tree model in appropriate size to be considered of having advantage in saving the time to find the similar cases.
Mi-Lim Choi, Myung Jae Lim, Young-Man Kwon and Dong-Kun Chung, 2017. A Study on the Prediction Method of Emergency Room (ER) Pollution Level based on Deep Learning using Scattering Sensor. Journal of Engineering and Applied Sciences, 12: 2560-2564.