Diagnosis of Covid-19 via patient breath data using artificial intelligence

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dc.authorid0000-0002-5971-9218
dc.authorid0000-0001-8863-8348
dc.authorid0000-0001-8359-5713
dc.authorid0000-0002-8985-8243
dc.authorid0000-0002-6872-1950
dc.authorid0000-0001-8022-7325
dc.contributor.authorDoğuç, Özge
dc.contributor.authorSilahtaroğlu, Gökhan
dc.contributor.authorCanbolat, Zehra Nur
dc.contributor.authorHambarde, Kailash
dc.contributor.authorYiğitbaşı, Ahmet Alperen
dc.contributor.authorGökay, Hasan
dc.contributor.authorYılmaz, Mesut
dc.date.accessioned2023-02-13T07:57:06Z
dc.date.available2023-02-13T07:57:06Z
dc.date.issued2023
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü
dc.description.abstractUsing machine learning algorithms for the rapid diagnosis and detection of the COVID-19 pandemic and isolating the patients from crowded environments are very important to controlling the epidemic. This study aims to develop a point-of-care testing (POCT) system that can detect COVID-19 by detecting volatile organic compounds (VOCs) in a patient's exhaled breath using the Gradient Boosted Trees Learner Algorithm. 294 breath samples were collected from 142 patients at Istanbul Medipol Mega Hospital between December 2020 and March 2021. 84 cases out of 142 resulted in negatives, and 58 cases resulted in positives. All these breath samples have been converted into numeric values through five air sensors. 10% of the data have been used for the validation of the model, while 75% of the test data have been used for training an AI model to predict the coronavirus presence. 25% have been used for testing. The SMOTE oversampling method was used to increase the training set size and reduce the imbalance of negative and positive classes in training and test data. Different machine learning algorithms have also been tried to develop the e-nose model. The test results have suggested that the Gradient Boosting algorithm created the best model. The Gradient Boosting model provides 95% recall when predicting COVID-19 positive patients and 96% accuracy when predicting COVID-19 negative patients.
dc.identifier.citationDoğuç, Ö., Silahtaroğlu, G., Canbolat, Z. N., Hambarde, K., Yiğitbaşı, A. A., Gökay, H. ... Yılmaz, M. (2023). Diagnosis of Covid-19 via patient breath data using artificial intelligence. Emerging Science Journal, 7, 105-113. https://dx.doi.org/10.28991/ESJ-2023-SPER-08
dc.identifier.doi10.28991/ESJ-2023-SPER-08
dc.identifier.endpage113
dc.identifier.issn2610-9182
dc.identifier.scopus2-s2.0-85147233491
dc.identifier.scopusqualityQ1
dc.identifier.startpage105
dc.identifier.urihttps://dx.doi.org/10.28991/ESJ-2023-SPER-08
dc.identifier.urihttps://hdl.handle.net/20.500.12511/10422
dc.identifier.volume7
dc.indekslendigikaynakScopus
dc.institutionauthorDoğuç, Özge
dc.institutionauthorSilahtaroğlu, Gökhan
dc.institutionauthorCanbolat, Zehra Nur
dc.institutionauthorYiğitbaşı, Ahmet Alperen
dc.institutionauthorGökay, Hasan
dc.institutionauthorYılmaz, Mesut
dc.language.isoen
dc.publisherItal Publication
dc.relation.ispartofEmerging Science Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsAttribution 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectArtificial Intelligence
dc.subjectBreath Data
dc.subjectCOVID-19
dc.subjectE-Nose
dc.subjectEpidemic Disease
dc.subjectMachine Learning
dc.titleDiagnosis of Covid-19 via patient breath data using artificial intelligence
dc.typeArticle

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