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Background: The SARS-CoV-2 virus has demonstrated the weakness of many health systems worldwide, creating a saturation and lack of access to treatments. A bottleneck to fight this pandemic relates to the lack of diagnostic infrastructure for early detection of positive cases, particularly in rural and impoverished areas of developing countries. In this context, less costly and fast machine learning (ML) diagnosis-based systems are helpful. However, most of the research has focused on deep-learning techniques for diagnosis, which are computationally and technologically expensive. ML models have been mainly used as a benchmark and are not entirely explored in the existing literature on the topic of this paper.

Objective: To analyze the capabilities of ML techniques (compared to deep learning) to diagnose COVID-19 cases based on X-ray images, assessing the performance of these techniques and using their predictive power for such a diagnosis. Methods: A factorial experiment was designed to establish this power with X-ray chest images of healthy, pneumonia, and COVID-19 infected patients. This design considers data-balancing methods, feature extraction approaches, different algorithms, and hyperparameter optimization. The ML techniques were evaluated based on classification metrics, including accuracy, the area under the receiver operating characteristic curve (AUROC), F1-score, sensitivity, and specificity. Results: The design of experiment provided the mean and its confidence intervals for the predictive capability of different ML techniques, which reached AUROC values as high as 90% with suitable sensitivity and specificity …