  Using machine learning algorithms for the rapid diagnosis and detection of
the COVID-19 pandemic and isolating the patients from crowded environments are
very important to controlling the epidemic. This study aims to develop a
point-of-care testing (POCT) system that can detect COVID-19 by detecting
volatile organic compounds (VOCs) in a patient's exhaled breath using the
Gradient Boosted Trees Learner Algorithm. 294 breath samples were collected
from 142 patients at Istanbul Medipol Mega Hospital between December 2020 and
March 2021. 84 cases out of 142 resulted in negatives, and 58 cases resulted in
positives. All these breath samples have been converted into numeric values
through five air sensors. 10% of the data have been used for the validation of
the model, while 75% of the test data have been used for training an AI model
to predict the coronavirus presence. 25% have been used for testing. The SMOTE
oversampling method was used to increase the training set size and reduce the
imbalance of negative and positive classes in training and test data. Different
machine learning algorithms have also been tried to develop the e-nose model.
The test results have suggested that the Gradient Boosting algorithm created
the best model. The Gradient Boosting model provides 95% recall when predicting
COVID-19 positive patients and 96% accuracy when predicting COVID-19 negative
patients.
