Using Artificial Intelligence for COVID-19 Detection in Blood Exams: A Comparative Analysis

COVID-19 is an infectious disease that was declared a pandemic by the World Health Organization (WHO) in early March 2020. Since its early development, it has challenged health systems around the world. Although more than 12 billion vaccines have been administered, at the time of writing, it has more than 623 million confirmed cases and more than 6 million deaths reported to the WHO. These numbers continue to grow, soliciting further research efforts to reduce the impacts of such a pandemic. In particular, artificial intelligence techniques have shown great potential in supporting the early diagnosis, detection, and monitoring of COVID-19 infections from disparate data sources. In this work, we aim to make a contribution to this field by analyzing a high-dimensional dataset containing blood sample data from over forty thousand individuals recognized as infected or not with COVID-19. Encompassing a wide range of methods, including traditional machine learning algorithms, dimensionality reduction techniques, and deep learning strategies, our analysis investigates the performance of different classification models, showing that accurate detection of blood infections can be obtained. In particular, an F-score of 84% was achieved by the artificial neural network model we designed for this task, with a rate of 87% correct predictions on the positive class. Furthermore, our study shows that the dimensionality of the original data, i.e. the number of features involved, can be significantly reduced to gain efficiency without compromising the final prediction performance. These results pave the way for further research in this field, confirming that artificial intelligence techniques may play an important role in supporting medical decision-making.