High performance COVID-19 screening using machine learning

##plugins.themes.academic_pro.article.sidebar##

Published Jan 2, 2025
https://doi.org/10.62438/tunismed.v103i1.5401
Download
pdf
Vol. 103 No. 1 (2025): Tunis Med Jan 2025

##plugins.themes.academic_pro.article.main##

Youssef Zied Elhechmi
Hope Horizon International
Mehdi Mrad
Laboratory of viruses, vectors and hosts: LR20IPT10, Institut Pasteur of Tunis, University of Tunis El Manar, 13, Place Pasteur, 1002 Tunis-Belvédère, Tunisia
Mariem Gdoura
Laboratory of Clinical Virology, Institut Pasteur of Tunis, University of Tunis El Manar, 13, Place Pasteur, 1002 Tunis-Belvédère, Tunisia.
Anissa Nouri
Clinical investigation center: 2016CICIPT02, Institut Pasteur of Tunis, University of Tunis El Manar, 13, Place Pasteur, 1002 Tunis-Belvédère, Tunisia.
Helmi Ben Saad
Physiology and Functional Explorations. Laboratory of Physiology and Functionnal Explorations. Farhat HACHED Hospital. Laboratory of Physiology. Faculty of Medicine of Sousse. Street Mohamed KAROUI. Sousse 4000. Tunisia.
Najla Ghrairi
Hope Horizon International
Henda Triki
Laboratory of viruses, vectors and hosts: LR20IPT10, Institut Pasteur of Tunis, University of Tunis El Manar, 13, Place Pasteur, 1002 Tunis-Belvédère, Tunisia

Abstract

Since the World Health Organization declared the Coronavirus Disease 2019 (COVID-19) pandemic as an international concern of public health emergency in the early 2020, several strategies have been initiated in many countries to prevent healthcare services breakdown and collapse of healthcare structures. The most important strategy was the increased testing, diagnosis, isolation, contact tracing to identify, quarantine and test close contacts. In this context, finding a rapid, reliable and affordable tool for COVID-19 screening was the main challenge to address the pandemic. Molecular diagnosis by reverse transcriptase polymerase chain reaction (RT-PCR), even though considered as the gold standard in the diagnosis of COVID-19, was time consuming and therefore does not fit the objective of rapid screening. In addition, serological tests to detect anti-severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) antibodies suffered from low sensitivity. Prediction models based on machine-learning (ML) that combined several clinical features to estimate the risk of COVID-19 have been developed. To address these screening challenges, we created a ML model (MLM) based on gradient boosting method. We included several clinical features and the daily geographic prevalence of COVID-19 cases in the MLM. The MLM was trained on 1554 cases (757 COVID-19), and tested on 547 cases (169 COVID-19). Our MLM successfully predicted RT-PCR positivity with an accuracy of 97.06%. Moreover, the variable sensitivity and specificity of our MLM depending on the disease geographic prevalence has introduced the concept of “dynamic” disease screening....(abstract truncated at 250 words).

Keywords:

Artificial intelligence, COVID-19, Machine learning, Mass screening, public health

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

  1. Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016. p. 785–94.
  2. Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure. 1975;405(2):442–51.
  3. Boughorbel S, Jarray F, El-Anbari M. Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS One. 2017 Jun 2;12(6):e0177678.
  4. Honein MA, Christie A, Rose DA, Brooks JT, Meaney-Delman D, Cohn A, et al. Summary of Guidance for Public Health Strategies to Address High Levels of Community Transmission of SARS-CoV-2 and Related Deaths, December 2020. MMWR Morb Mortal Wkly Rep. 2020 Dec 11;69(49):1860–7.
  5. Recommendations for national SARS-CoV-2 testing strategies and diagnostic capacities [Internet]. [cited 2024 May 20]. Available from: https://www.who.int/publications-detail-redirect/WHO-2019-nCoV-lab-testing-2021.1-eng
  6. Pullen MF, Skipper CP, Hullsiek KH, Bangdiwala AS, Pastick KA, Okafor EC, et al. Symptoms of COVID-19 Outpatients in the United States. Open Forum Infectious Diseases. 2020 Jul 1;7(7):ofaa271.
  7. Mehta OP, Bhandari P, Raut A, Kacimi SEO, Huy NT. Coronavirus Disease (COVID-19): Comprehensive Review of Clinical Presentation. Front Public Health [Internet]. 2021 Jan 15 [cited 2024 Oct 15];8. Available from: https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2020.582932/full
  8. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. New England Journal of Medicine. 2020 Feb 20;382(8):727–33.
  9. Núñez I, Belaunzarán-Zamudio PF, Caro-Vega Y. Result Turnaround Time of RT-PCR for SARS-CoV-2 is the Main Cause of COVID-19 Diagnostic Delay: A Country-Wide Observational Study of Mexico and Colombia. Rev Invest Clin. 2022 Mar 15;74(2):071–80.
  10. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China. JAMA. 2020 Mar 17;323(11):1061–9.
  11. Döhla M, Boesecke C, Schulte B, Diegmann C, Sib E, Richter E, et al. Rapid point-of-care testing for SARS-CoV-2 in a community screening setting shows low sensitivity. Public Health. 2020 May 1;182:170–2.
  12. Zied Elhechmi Y. Medicine at the dawn of Artificial Intelligence. Tunis Med. 2022 May;100(5):354–5.
  13. Elaziz MA, Hosny KM, Salah A, Darwish MM, Lu S, Sahlol AT. New machine learning method for image-based diagnosis of COVID-19. Plos one. 2020;15(6):e0235187.
  14. Ahamad MdM, Aktar S, Rashed-Al-Mahfuz Md, Uddin S, Liò P, Xu H, et al. A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients. Expert Systems with Applications. 2020 Dec 1;160:113661.
  15. Lalmuanawma S, Hussain J, Chhakchhuak L. Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals. 2020 Oct 1;139:110059.
  16. de Moraes Batista AF, Miraglia JL, Donato THR, Chiavegatto Filho ADP. COVID-19 diagnosis prediction in emergency care patients: a machine learning approach. medRxiv. 2020;
  17. Zoabi Y, Deri-Rozov S, Shomron N. Machine learning-based prediction of COVID-19 diagnosis based on symptoms. npj digital medicine. 2021;4(1):1–5.
  18. Garcia LP, Goncalves AV, de Andrade MP, Pedebos LA, Vidor AC, Zaina R, et al. Estimating underdiagnosis of covid-19 with nowcasting and machine learning: Experience from brazil. medRxiv. 2020;
  19. Mei X, Lee HC, Diao K yue, Huang M, Lin B, Liu C, et al. Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nature medicine. 2020;26(8):1224–8.
  20. Li WT, Ma J, Shende N, Castaneda G, Chakladar J, Tsai JC, et al. Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis. BMC medical informatics and decision making. 2020;20(1):1–13.
  21. Avila E, Kahmann A, Alho C, Dorn M. Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios. PeerJ. 2020;8:e9482.
  22. Laboratory biosafety guidance related to coronavirus disease (COVID-19) [Internet]. [cited 2021 Jul 31]. Available from: https://www.who.int/publications-detail-redirect/laboratory-biosafety-guidance-related-to-coronavirus-disease-(covid-19)
  23. whoinhouseassays.pdf [Internet]. [cited 2021 Jul 31]. Available from: https://www.who.int/docs/default-source/coronaviruse/whoinhouseassays
  24. Organization WH. Laboratory testing for 2019 novel coronavirus (2019-nCoV) in suspected human cases: interim guidance, 17 January 2020. World Health Organization; 2020. 7 p.
  25. Epi InfoTM | CDC [Internet]. 2019 [cited 2021 Jul 31]. Available from: https://www.cdc.gov/epiinfo/index.html
  26. Karim M, Rahman RM. Decision tree and naive bayes algorithm for classification and generation of actionable knowledge for direct marketing. 2013;
  27. Dzieciatkowski T, Szarpak L, Filipiak KJ, Jaguszewski M, Ladny JR, Smereka J. COVID-19 challenge for modern medicine. Cardiology Journal. 2020;27(2):175–83.
  28. Sohrabi C, Alsafi Z, O’neill N, Khan M, Kerwan A, Al-Jabir A, et al. World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). International journal of surgery. 2020;76:71–6.
  29. Stokes K, Castaldo R, Federici C, Pagliara S, Maccaro A, Cappuccio F, et al. The use of artificial intelligence systems in diagnosis of pneumonia via signs and symptoms: A systematic review. Biomedical Signal Processing and Control. 2022 Feb 1;72:103325.
  30. Mrabet S, Aloui K, Ben Jazia E. Development of a Web Application based on Machine Learning for screening esophageal varices in cirrhosis. Tunis Med. 2023;101(8–9):684–7.
  31. Okiyama S, Fukuda M, Sode M, Takahashi W, Ikeda M, Kato H, et al. Examining the Use of an Artificial Intelligence Model to Diagnose Influenza: Development and Validation Study. Journal of Medical Internet Research. 2022 Dec 23;24(12):e38751.
  32. Ozsahin I, Sekeroglu B, Musa MS, Mustapha MT, Uzun Ozsahin D. Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence. Comput Math Methods Med. 2020 Sep 26;2020:9756518.
  33. Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, et al. Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv preprint arXiv:200305037. 2020;
  34. Ying S, Zheng S, Li L, Zhang X, Zhang X, Huang Z, et al. Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images. medRxiv. 2020 Feb 25;2020.02.23.20026930.
  35. Tostmann A, Bradley J, Bousema T, Yiek WK, Holwerda M, Bleeker-Rovers C, et al. Strong associations and moderate predictive value of early symptoms for SARS-CoV-2 test positivity among healthcare workers, the Netherlands, March 2020. Eurosurveillance. 2020 Apr 23;25(16):2000508.