Show simple item record

A machine learning approach as an aid for early covid-19 detection

dc.contributor.authorMartinez-Velazquez R
dc.contributor.authorTobón V. D.P
dc.contributor.authorSanchez A
dc.contributor.authorEl Saddik A
dc.contributor.authorPetriu E.
dc.date.accessioned2022-09-14T14:33:26Z
dc.date.available2022-09-14T14:33:26Z
dc.date.created2021
dc.identifier.issn14248220
dc.identifier.urihttp://hdl.handle.net/11407/7364
dc.descriptionThe novel coronavirus SARS-CoV-2 that causes the disease COVID-19 has forced us to go into our homes and limit our physical interactions with others. Economies around the world have come to a halt, with non-essential businesses being forced to close in order to prevent further propagation of the virus. Developing countries are having more difficulties due to their lack of access to diagnostic resources. In this study, we present an approach for detecting COVID-19 infections exclusively on the basis of self-reported symptoms. Such an approach is of great interest because it is relatively inexpensive and easy to deploy at either an individual or population scale. Our best model delivers a sensitivity score of 0.752, a specificity score of 0.609, and an area under the curve for the receiver operating characteristic of 0.728. These are promising results that justify continuing research efforts towards a machine learning test for detecting COVID-19. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.eng
dc.language.isoeng
dc.publisherMDPI AG
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85108064294&doi=10.3390%2fs21124202&partnerID=40&md5=3e8e58ec60b9e51af0ec3cb1c8ebea2e
dc.sourceSensors
dc.titleA machine learning approach as an aid for early covid-19 detection
dc.typeArticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Telecomunicaciones
dc.type.spaArtículo
dc.identifier.doi10.3390/s21124202
dc.subject.keywordCOVID-19 detectioneng
dc.subject.keywordMachine learningeng
dc.subject.keywordSARS-CoV-2eng
dc.subject.keywordSymptoms-based testeng
dc.subject.keywordDeveloping countrieseng
dc.subject.keywordDiagnosiseng
dc.subject.keywordDiseaseseng
dc.subject.keywordTuring machineseng
dc.subject.keywordViruseseng
dc.subject.keywordArea under the curveseng
dc.subject.keywordBest modeleng
dc.subject.keywordCoronaviruseseng
dc.subject.keywordMachine learning approacheseng
dc.subject.keywordNon essentialeng
dc.subject.keywordPhysical interactionseng
dc.subject.keywordReceiver operating characteristicseng
dc.subject.keywordResearch effortseng
dc.subject.keywordMachine learningeng
dc.relation.citationvolume21
dc.relation.citationissue12
dc.publisher.facultyFacultad de Ingenierías
dc.affiliationMartinez-Velazquez, R., School of Electrical Engineering and Computer Science, University of Ottawa, 75 Laurier Ave. E, Ottawa, ON K1N 6N5, Canada
dc.affiliationTobón V., D.P., Faculty of Engineering, Universidad de Medellín, Carrera 87 No. 30-65, Medellin, 050010, Colombia
dc.affiliationSanchez, A., Department of Information Technology, University of Colima, Avenida Universidad 333, Las Viboras, Colima, Col., 28040, Mexico
dc.affiliationEl Saddik, A., School of Electrical Engineering and Computer Science, University of Ottawa, 75 Laurier Ave. E, Ottawa, ON K1N 6N5, Canada
dc.affiliationPetriu, E., School of Electrical Engineering and Computer Science, University of Ottawa, 75 Laurier Ave. E, Ottawa, ON K1N 6N5, Canada
dc.relation.referencesElflein, J., (2021) Coronavirus (COVID-19) disease pandemic-Statistics & Facts|Statista, , https://www.sta-tista.com/topics/5994/the-coronavirus-disease-covid-19-outbreak/, (accessed on 30 April 2021)
dc.relation.referencesBracis, C., Burns, E., Moore, M., Swan, D., Reeves, D.B., Schiffer, J.T., Dimitrov, D., Widespread testing, case isolation and contact tracing may allow safe school reopening with continued moderate physical distancing: A modeling analysis of King County, WA data (2021) Infect. Dis. Model, 6, pp. 24-35
dc.relation.referencesFerraresi, M., Migali, G., Rizzo, L., Secomandi, R., Widespread swabs testing and the fight against the Covid-19 outbreak (2021) Reg. Stud. Reg. Sci, 8, pp. 85-87
dc.relation.referencesRosenberg, E.S., Holtgrave, D.R., Widespread and Frequent Testing is Essential to Controlling Coronavirus Disease 2019 (COVID-19) in the United States (2020) Clin. Infect. Dis
dc.relation.referencesThunström, L., Ashworth, M., Shogren, J.F., Newbold, S., Finnoff, D., Testing for COVID-19: willful ignorance or selfless behavior? (2021) Behav. Public Policy, 5, pp. 135-152
dc.relation.referencesFouda, A., Mahmoudi, N., Moy, N., Paolucci, F., The COVID-19 pandemic in Greece, Iceland, New Zealand, and Singapore: Health policies and lessons learned (2020) Health Policy Technol, 9, pp. 510-524
dc.relation.referencesSummers, J., Cheng, H.-Y., Lin, P.H.-H., Barnard, L.T., Kvalsvig, A., Wilson, P.N., Baker, P.M.G., Potential lessons from the Taiwan and New Zealand health responses to the COVID-19 pandemic (2020) Lancet Reg. Health West. Pac, 4, p. 100044
dc.relation.referencesDowdy, D., D’souza, G., (2020) COVID-19 Testing: Understanding the ‘Percent Positive’—COVID-19—Johns Hopkins Bloom-berg School of Public Health, , https://www.jhsph.edu/covid-19/articles/covid-19-testing-understanding-the-percent-positive.html, (accessed on 30 April 2021)
dc.relation.referencesScudellari, M., How Iceland hammered COVID with science (2020) Nat. Cell Biol, 587, pp. 536-539
dc.relation.referencesChen, C.-C., Tseng, C.-Y., Choi, W.-M., Lee, Y.-C., Su, T.-H., Hsieh, C.-Y., Chang, C.-M., Tai, Y.-L., Taiwan Government-Guided Strategies Contributed to Combating and Controlling COVID-19 Pandemic (2020) Front. Public Health, 8, p. 547423
dc.relation.referencesRate of Coronavirus (COVID-19) Tests Performed in the most Impacted Countries Worldwide as of 12 April 2020 (per Million Population), , https://www.statista.com/statistics/1104645/covid19-testing-rate-select-countries-world-wide/, Statista. (accessed on 12 April 2020)
dc.relation.referencesHasell, J., Mathieu, E., Beltekian, D., Macdonald, B., Giattino, C., Ortiz-Ospina, E., Roser, M., Ritchie, H., A cross-country data-base of COVID-19 testing (2020) Sci. Data, 7, pp. 1-7
dc.relation.referencesPeto, J., Covid-19 mass testing facilities could end the epidemic rapidly (2020) BMJ, 368, p. m1163
dc.relation.referencesCohen, J., Kupferschmidt, K., Countries test tactics in ‘war’ against COVID-19 (2020) Science, 367, pp. 1287-1288
dc.relation.referencesBalilla, J., Assessment of COVID-19 Mass Testing: The Case of South Korea (2020) SSRN Electron. J
dc.relation.referencesWang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., Zheng, C., A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization from Chest CT (2020) IEEE Trans. Med Imaging, 39, pp. 2615-2625
dc.relation.referencesFan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., Shao, L., Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images (2020) IEEE Trans. Med Imaging, 39, pp. 2626-2637
dc.relation.referencesRoy, S., Menapace, W., Oei, S., Luijten, B., Fini, E., Saltori, C., Huijben, I., Sentelli, A., Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound (2020) IEEE Trans. Med Imaging, 39, pp. 2676-2687
dc.relation.referencesDas, N.N., Kumar, N., Kaur, M., Kumar, V., Singh, D., Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays (2020) IRBM
dc.relation.referencesChollet, F., Xception: Deep Learning with Depthwise Separable Convolutions Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1251-1258. , Honolulu, HI, USA, 21–26 July 2017
dc.relation.referencesSethi, R., Mehrotra, M., Sethi, D., Deep Learning based Diagnosis Recommendation for COVID-19 using Chest X-rays Images Proceedings of the 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 1-4. , Coimbatore, India, 15–17 July 2020
dc.relation.referencesHoward, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H., (2017) MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, , http://arxiv.org/abs/1704.04861, arXiv (accessed on 3 May 2021)
dc.relation.referencesAbbasi, W.A., Abbas, S.A., Andleeb, S., COVIDX: Computer-aided diagnosis of Covid-19 and its severity prediction with raw digital chest X-ray images, , http://arxiv.org/abs/2012.13605, arXiv 2020. (accessed on 31 May 2021)
dc.relation.referencesAbdulkareem, K.H., Mohammed, M.A., Salim, A., Arif, M., Geman, O., Gupta, D., Khanna, A., Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IOT in Smart Hospital Environment (2021) IEEE Internet Things J, p. 1
dc.relation.referencesSchwab, P., Schütte, A.D., Dietz, B., Bauer, S., Clinical Predictive Models for COVID-19: Systematic Study (2020) J. Med. Internet Res, 22, p. e21439
dc.relation.referencesAlakus, T.B., Turkoglu, I., Comparison of deep learning approaches to predict COVID-19 infection (2020) Chaos Solitons Fractals, 140, p. 110120
dc.relation.referencesMardani, R., Vasmehjani, A.A., Zali, F., Gholami, A., Nasab, S.D.M., Kaghazian, H., Kaviani, M., Ahmadi, N., Laboratory Parameters in Detection of COVID-19 Patients with Positive RT-PCR
dc.relation.referencesa Diagnostic Accuracy Study (2020) Arch. Acad. Emerg. Med, 8, p. e43
dc.relation.referencesCallahan, A., Steinberg, E., Fries, J.A., Gombar, S., Patel, B., Corbin, C.K., Shah, N.H., Estimating the efficacy of symptom-based screening for COVID-19 (2020) NPJ Digit. Med, 3, pp. 1-3
dc.relation.referencesQuer, G., Radin, J.M., Gadaleta, M., Baca-Motes, K., Ariniello, L., Ramos, E., Kheterpal, V., Steinhubl, S.R., Wearable sensor data and self-reported symptoms for COVID-19 detection (2021) Nat. Med, 27, pp. 73-77
dc.relation.referencesHan, J., Brown, C., Chauhan, J., Grammenos, A., Hasthanasombat, A., Spathis, D., Xia, T., Mascolo, C., Exploring Automatic COVID-19 Diagnosis via Voice and Symptoms from Crowdsourced Data, , http://arxiv.org/abs/2102.05225, February 2021. (accessed on 4 May 2021)
dc.relation.referencesSudre, C.H., Lee, K.A., Ni Lochlainn, M., Varsavsky, T., Murray, B., Graham, M.S., Menni, C., Nguyen, L.H., Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app (2021) Sci. Adv, 7, p. eabd4177
dc.relation.referencesPedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Dubourg, V., Scikit-learn: Machine Learning in Python (2011) J. Mach. Learn. Res, 12, pp. 2825-2830
dc.relation.referencesFlach, P., The ingredients of machine learning (2012) Machine Learning, pp. 13-48. , Cambridge University Press (CUP): Cambridge, UK
dc.relation.referencesBreiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., (2017) Classification and Regression Trees, , CRC Press: Boca Raton, FL, USA
dc.relation.referencesKingma, D.P., Ba, J., Adam: A method for stochastic optimization (2015) Proceedings of the International Conference Learn. Represent. (ICLR), , San Diego, CA, USA, 5–8 May
dc.relation.referenceshttps://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symp-toms.html, Symptoms of Coronavirus|CDC. (accessed on 6 May 2021)
dc.relation.referencesBergstra, J., Ca, J.B., Ca, Y.B., (2012) Random Search for Hyper-Parameter Optimization Yoshua Bengio, , http://scikit-learn.sourceforge.net, (accessed on 6 May 2021)
dc.type.coarhttp://purl.org/coar/resource_type/c_6501
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/article
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record