TIME SERIES OUTLIER ANALYSIS FOR MODEL, DATA AND HUMAN-INDUCED RISKSIN COVID-19 SYMPTOMS DETECTION
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Information systems are important references aiming to support the decisions of decision makers. Information reliability depends on the accuracy and efficacy of data and models. Therefore,some risks may emerge in information systems concerning models, data, and humans. It is important toidentify and extract outliers in decision support systems developed for the health information systemssuch as the detection system of Covid-19 symptoms. In this study, the risks that are important in decisionmaking in Covid-19 symptom detection were determined by the statistical time series (ARMA) approach.Potential solutions are proposed in this way. Moreover, outliers are detected by software developed byusing the Box-Jenkins model, and the reliability and accuracy of data are increased by using estimateddata instead of outliers. In the implementation of this study, time-series-based data obtained fromlaboratory examinations of Covid-19 test devices can be used. With the method revealed here, outliersoriginating from healthcare workers or test apparatus can be detected and more accurate results canbe obtained by replacing these outliers with estimated values.
Açıklama
Anahtar Kelimeler
Kaynak
Middle East Journal of Science
WoS Q Değeri
Scopus Q Değeri
Cilt
7
Sayı
2
