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...