Artificial Intelligence-Based Prediction of Covid-19 Severity on the Results of Protein Profiling

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dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridYASAR, Seyma/0000-0003-1300-3393
dc.authorwosidYaşar, Şeyma/ABI-8055-2020
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.contributor.authorYasar, Seyma
dc.contributor.authorColak, Cemil
dc.contributor.authorYologlu, Saim
dc.date.accessioned2024-08-04T20:49:18Z
dc.date.available2024-08-04T20:49:18Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground: COVID-19 progresses slowly and negatively affects many people. However, mild to moderate symptoms develop in most infected people, who recover without hospitalization. Therefore, the development of early diagnosis and treatment strategies is essential. One of these methods is proteomic technology based on the blood protein profiling technique. This study aims to classify three COVID-19 positive patient groups (mild, severe, and critical) and a control group based on the blood protein profiling using deep learning (DL), random forest (RF), and gradient boosted trees (GBTs). Methods: The dataset consists of 93 samples (60 COVID-19 patients, 33 control), and 370 variables obtained from an open-source website. The current dataset contains age, gender, and 368 protein, used to predict the relationship between disease severity and proteins using DL and machine learning approaches (RF, GBTs). An evolutionary algorithm tunes hyperparameters of the models and the predictions are assessed through accuracy, sensitivity, specificity, precision, F1 score, classification error, and kappa performance metrics. Results: The accuracy of RF (96.21%) was higher as compared to DL (94.73%). However, the ensemble classifier GBTs produced the highest accuracy (96.98%). TGB1BP2 in the cardiovascular II panel and MILR1 in the inflammation panel were the two most important proteins associated with disease severity. Conclusions: The proposed model (GBTs) achieved the best prediction of disease severity based on the proteins compared to the other algorithms. The results point out that changes in blood proteins associated with the severity of COVID-19 may be used in monitoring and early diagnosis/treatment of the disease. Background: COVID-19 progresses slowly and negatively affects many people. However, mild to moderate symptoms develop in most infected people, who recover without hospitalization. Therefore, the development of early diagnosis and treatment strategies is essential. One of these methods is proteomic technology based on the blood protein profiling technique. This study aims to classify three COVID-19 positive patient groups (mild, severe, and critical) and a control group based on the blood protein profiling using deep learning (DL), random forest (RF), and gradient boosted trees (GBTs). Methods: The dataset consists of 93 samples (60 COVID-19 patients, 33 control), and 370 variables obtained from an open-source website. The current dataset contains age, gender, and 368 protein, used to predict the relationship between disease severity and proteins using DL and machine learning approaches (RF, GBTs). An evolutionary algorithm tunes hyperparameters of the models and the predictions are assessed through accuracy, sensitivity, specificity, precision, F1 score, classification error, and kappa performance metrics. Results: The accuracy of RF (96.21%) was higher as compared to DL (94.73%). However, the ensemble classifier GBTs produced the highest accuracy (96.98%). TGB1BP2 in the cardiovascular II panel and MILR1 in the inflammation panel were the two most important proteins associated with disease severity. Conclusions: The proposed model (GBTs) achieved the best prediction of disease severity based on the proteins compared to the other algorithms. The results point out that changes in blood proteins associated with the severity of COVID-19 may be used in monitoring and early diagnosis/treatment of the disease. ? 2021 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.cmpb.2021.105996
dc.identifier.issn0169-2607
dc.identifier.issn1872-7565
dc.identifier.pmid33631640en_US
dc.identifier.scopus2-s2.0-85101141535en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.cmpb.2021.105996
dc.identifier.urihttps://hdl.handle.net/11616/99774
dc.identifier.volume202en_US
dc.identifier.wosWOS:000639096300006en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofComputer Methods and Programs in Biomedicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectCOVID-19en_US
dc.subjectRandom Foresten_US
dc.subjectDeep Learningen_US
dc.subjectGradient Boosted Treesen_US
dc.titleArtificial Intelligence-Based Prediction of Covid-19 Severity on the Results of Protein Profilingen_US
dc.typeArticleen_US

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