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Activity Number: 319 - SLDS CSpeed 6
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #317946
Title: Using Machine Learning Techniques to Model Factors That Influence the Intent of a Person to Take a Coronavirus Test
Author(s): Sheila Rutto*
Companies: The University of Texas Rio Grande Valley
Keywords: AUC; ANN; COVID-19; Coronavirus

Emerging of the COVID-19 pandemic encouraged rapid studies to understand its cause, risk factors, symptoms, and mitigation that, can be taken against coronavirus. The US Department of Health and Human Services is leading in advocating for Americans to be tested for the coronavirus as it is an essential step in fighting the virus. Fighting against coronavirus testing is vital but, it is an individual choice to take a test or not. This study attempts to establish a prediction model using the gradient boosting and artificial neural network (ANN) technique to establish some background factors about a person that can influence their choice to take the COVID-19 test. Using COVID-19 survey data from the Understanding American Study website, several models were fitted using different machine learning techniques. The gradient boosting and ANN models emerged to be the best in terms of model performance, with AUC values of 0.7409 and 0.9525, respectively. The model was trained and tested using 280 and 120 observations, respectively. Given such high model performances, these models can be used in predicting if an individual can take a corona test or not, which can be useful information.

Authors who are presenting talks have a * after their name.

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