Factors Predicting Acceptance and Recommendation of Covid-19 Vaccines Among Previously Infected Academic Dental Hospital Personnel; An Artificial Intelligence-Based Study

Abstract

Abstract Objectives The study aims to construct artificial neural networks that are capable of predicting willingness of previously infected academic dental hospital personnel (ADHP) to accept or recommend vaccines to family or patients. Methods: The study utilized data collected during a cross-sectional survey conducted among COVID-19 infected ADHP. A total of ten variables were used as input variables for the network and analysis was repeated 10 times to calculate variation in accuracy and validity of input variables. Three variables were determined by the best network to be the least important and consequently they were excluded and a new network was constructed using the remaining seven variables. Analysis was repeated 10 times to investigate variation of accuracy of predictions. Results: The best network showed a prediction accuracy that exceeded 90% during testing stage. This network was used to predict attitudes towards vacci-nation for a number of hypothetical subjects. The following factors were identified as predictors for undesirable vaccination attitudes: dental students who had an insufficient vaccine awareness, a long symptomatic period of illness, and who did not practice quarantine. Conclusions: It is concluded that vaccine awareness is the most important factor in predicting favorable vaccine attitudes. Vaccine awareness campaigns that target ADHP should give more attention to students than their faculty.