Evaluation of the prediction of CoVID-19 recovered and unrecovered cases using symptoms and patient's meta data based on support vector machine, neural network, CHAID and QUEST Models

Basit Öğe Kaydı
dc.authoridAL Najjar, Dana/0000-0001-7292-1536
dc.contributor.authorAl-Najjar, D.
dc.contributor.authorAl-Najjar, H.
dc.contributor.authorAl-Rousan, Nadia
dc.date.accessioned2024-09-11T19:53:04Z
dc.date.available2024-09-11T19:53:04Z
dc.date.issued2021
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractOBJECTIVE: This paper aims to develop four prediction models for recovered and unrecovered cases using descriptive data of patients and symptoms of CoVID-19 patients. The developed prediction models aim to extract the important variables in predicting recovered cases by using the binary values for recovered cases. MATERIALS AND METHODS: The data were collected from different countries all over the world. The input of the prediction model contains 28 symptoms and four variables of the patient's information. Symptoms of COVID-19 include a high fever, low fever, sore throat, cough, and so on, where patient metadata includes Province, county, sex, and age. The dataset contains 1254 patients with 664 recovered cases. To develop prediction models, four models are used including neural network, support vector machine, CHAID, and QUEST models. To develop prediction models, the dataset is divided into train and test datasets with splitting ratios equal to 70%, and 30%, respectively. RESULTS: The results showed that the neural network model is the most effective model in developing COVID-19 prediction with the highest performance metrics using train and test datasets. The results found that recovered cases are associated with the place of the patients mainly, province of the patient. Besides the results showed that high fever is not strongly associated with recovered cases, where cough and low fever are strongly associated with recovered cases. In addition, the country, sex, and age of the patients have higher importance than other patient's symptoms in COVID-19 development. CONCLUSIONS: The results revealed that the prediction models of the recovered COVID-19 cases can be effectively predicted using patient characteristics and symptoms, besides the neural network model is the most effective model to create a COVID -19 prediction model. Finally, the research provides empirical evidence that recovered cases of COVID-19 are closely related to patients' provinces.en_US
dc.identifier.doi10.26355/eurrev_202109_26668
dc.identifier.endpage5560en_US
dc.identifier.issn1128-3602
dc.identifier.issue17en_US
dc.identifier.pmid34533806en_US
dc.identifier.scopus2-s2.0-85114801967en_US
dc.identifier.startpage5556en_US
dc.identifier.urihttps://doi.org/10.26355/eurrev_202109_26668
dc.identifier.urihttps://hdl.handle.net/11363/8048
dc.identifier.volume25en_US
dc.identifier.wosWOS:000695653800032en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherVerduci Publisheren_US
dc.relation.ispartofEuropean Review For Medical And Pharmacological Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectEpidemiologyen_US
dc.subjectSymptomsen_US
dc.subjectInfectionen_US
dc.subjectCOVID-19en_US
dc.subjectMachine learningen_US
dc.titleEvaluation of the prediction of CoVID-19 recovered and unrecovered cases using symptoms and patient's meta data based on support vector machine, neural network, CHAID and QUEST Modelsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Makale / Article
Boyut:
571.39 KB
Biçim:
Adobe Portable Document Format
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu