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Machine Learning Models for Early Prediction of COVID-19 Infections Based on Clinical Signs

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Abstract

Nowadays, the appearance of common symptoms, such as cough, fever, and loss of smell and taste, is the starting point of a battle against the coronavirus. The first standard method of COVID-19 infection assertion has become the RT-PCR test, which is however an uncomfortable solution for both patients and medical staff due to its high cost, timeliness, and false-negative result issue. This has raised the need for reliable automatic detection systems that aid in the early prediction of the COVID-19 infections with a lower cost. In this work, we aim at profiting from the Machine Learning (ML) advances to provide a reliable and low-cost COVID-19 prediction system. This system is based on the disease starting point, which is the patients’ clinical symptoms, that are still under-explored. We developed seven predictive models using traditional ML classification algorithms using a public dataset of obvious high-risk factors from patients’ clinical signs. The dataset has first undergone a pre-processing phase consisting of feature engineering and dataset resampling to deal with imbalanced dataset issue. Our best classification model is able to detect true positives and true negatives and weed out false positive and false negatives with an accuracy of 93%.

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Data Availability Statement

Publicly available datasets were used in this study. The executable, source code and data are available at: https://github.com/boulbaba1981/COVID-19.

Notes

  1. Coronavirus Disease 2019 Clinical Data Repository. Accessed from https://covidclinicaldata.org/.

  2. Where TP is the true positive, TN is the true negative, FP is the false positive and FN is the false-negative.

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Funding

This work was supported by the Ministry of Higher Education and Scientific Research in Tunisia (MoHESR) as part of the Federated Research Project PRFCOV19-D1-P1.

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Authors and Affiliations

  1. Data Engineering and Semantics Research Unit, Faculty of Science, University of Sfax, Sfax, Tunisia

    Boulbaba Ben Ammar, Ali Salem & Mohamed Ben Aouicha

  2. Digital Research Center of Sfax, Ministry of Higher Education and Scientific Research, Sfax, Tunisia

    Mouna Ben Said

  3. Signals systeMs aRtificial intelligence and neTworkS Laboratory (SM@RTS), Digital Research Center of Sfax, Sfax, Tunisia

    Mouna Ben Said

Authors
  1. Boulbaba Ben Ammar
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  2. Ali Salem
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  4. Mohamed Ben Aouicha
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Correspondence to Boulbaba Ben Ammar.

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The authors declare that they have no conflict of interest.

Ethical Considerations and Consent to Participate

In this study, we used a publicly available dataset of clinical characteristics of patients who have taken a COVID-19 test https://covidclinicaldata.org/.

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Ben Ammar, B., Salem, A., Ben Said, M. et al. Machine Learning Models for Early Prediction of COVID-19 Infections Based on Clinical Signs. SN COMPUT. SCI. 5, 158 (2024). https://doi.org/10.1007/s42979-023-02489-3

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