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Original Research

Open Access Special Issue

Machine learning techniques as an efficient alternative diagnostic tool for COVID-19 cases

  • Nicolás Bustos1
  • Manuel Tello1
  • Guillermo Droppelmann2,3
  • Nicolás García2,4
  • Felipe Feijoo1
  • Víctor Leiva1

1School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile

2Academic Unit, Clínica MEDS, 7691236 Santiago, Chile

3Health Sciences Ph.D. Program, Universidad Católica de Murcia, 30107 Murcia, Spain

4Departments of Radiology, Clínica MEDS, 7691236 Santiago, Chile

DOI: 10.22514/sv.2021.110 Vol.18,Issue 1,January 2022 pp.23-33

Submitted: 07 April 2021 Accepted: 14 May 2021

Published: 08 January 2022

*Corresponding Author(s): Víctor Leiva E-mail:


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 hyper-parameter 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. Among the learning algorithms, support vector machines and random forest performed best. The down-sampling method for unbalanced data improved the predictive power significantly for the images used in this study.

Conclusions: Our investigation demonstrated that ML techniques are able to identify COVID-19 infected patients. The results provided suitable values of sensitivity and specificity, minimizing the false-positive or false-negative rates. The models were trained with significantly low computational resources, which helps to provide access and deployment in rural and impoverished areas.


Artificial intelligence; Deep learning; PCR; ROC curve; R software; SARS-CoV-2; X-rays

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Nicolás Bustos,Manuel Tello,Guillermo Droppelmann,Nicolás García,Felipe Feijoo,Víctor Leiva. Machine learning techniques as an efficient alternative diagnostic tool for COVID-19 cases. Signa Vitae. 2022. 18(1);23-33.


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