ThaiScience  


ENIVRONMENT ASIA


Volume 14, No. 03, Month SEPTEMBER, Year 2021, Pages 91 - 101


An artificial neural network prediction model of respiratory illness among medical students during gross anatomy dissection classes

Arroon Ketsakorn, Saowanee Norkaew, Kanjana Changkaew, Chalermchai Chaikittiporn, Vanusaya Su-angkavatin, Pannathorn Thammabut, and Ratchapong Chaiyadej


Abstract Download PDF

Exposure to indoor air pollutants can cause adverse health outcomes. This study aimed to develop an Artifi cial Neural Network (ANN) model to predict respiratory illness among students during gross anatomy dissection classes. All participants were interviewed face-to-face using questionnaires. General information, gross anatomy laboratory room characteristics, and symptoms of respiratory illness during gross anatomy dissection were assessed. The environmental parameters related to indoor air quality, total fungi, and bacteria in a gross anatomy dissection room were measured. Pearson’s correlation, spearman’s rank correlation and regression analysis were used to analyse data. The fi ndings revealed ten factors signifi cantly associated with respiratory illness (P < 0.05). The six infl uencing variables including formaldehyde concentration (personal sampling), bacteria, relative humidity, fungi, time of gross anatomy dissection class, and formaldehyde concentration (area sampling) as tested using regression analysis. ANN model was then run to predict the respiratory illness from those six variables. Predictive accuracy was assessed by the Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Root Mean Square Error (RMSE) value.etc...


Keywords

: Respiratory illness; Indoor air quality; Artificial neural network model; Gross anatomy dissection



ENIVRONMENT ASIA


Published by : Thai Society of Higher Education Institutes on Environment
Contributions welcome at : http://www.tshe.org/en/