Novel hybridized computational paradigms integrated with five stand-alone algorithms for clinical prediction of HCV status among patients: A data-driven technique

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dc.authorid0000-0002-6141-8676
dc.contributor.authorMadaki, Zachariah
dc.contributor.authorAbacıoğlu, Nurettin
dc.contributor.authorUsman?, ?Abdullahi Garba
dc.contributor.authorTaner, Neda
dc.contributor.authorŞehirli, Ahmet Özer
dc.contributor.authorAbba, Sani Isah
dc.date.accessioned2023-02-16T11:21:06Z
dc.date.available2023-02-16T11:21:06Z
dc.date.issued2023
dc.departmentİstanbul Medipol Üniversitesi, Eczacılık Fakültesi, Eczacılık Meslek Bilimleri Bölümü, Klinik Eczacılık Ana Bilim Dalı
dc.description.abstractThe emergence of health informatics opens new opportunities and doors for different disease diagnoses. The current work proposed the implementation of five different stand-alone techniques coupled with four different novel hybridized paradigms for the clinical prediction of hepatitis C status among patients, using both sociodemographic and clinical input variables. Both the visualized and quantitative performances of the stand-alone algorithms present the capability of the Gaussian process regression (GPR), Generalized neural network (GRNN), and Interactive linear regression (ILR) over the Support Vector Regression (SVR) and Adaptive neuro-fuzzy inference system (ANFIS) models. Hence, due to the lower performance of the stand-alone algorithms at a certain point, four different novel hybrid data intelligent algorithms were proposed, including: interactive linear regression-Gaussian process regression (ILR-GPR), interactive linear regression-generalized neural network (ILR-GRNN), interactive linear regression-Support Vector Regression (ILR-SVR), and interactive linear regression-adaptive neuro-fuzzy inference system (ILR-ANFIS), to boost the prediction accuracy of the stand-alone techniques in the clinical prediction of hepatitis C among patients. Based on the quantitative prediction skills presented by the novel hybridized paradigms, the proposed techniques were able to enhance the performance efficiency of the single paradigms up to 44% and 45% in the calibration and validation phases, respectively.
dc.description.sponsorshipOperational Research Centre in Healthcare, Near East University, North Cyprus, Mersin-10, Turkiyeen_US
dc.identifier.citationMadaki, Z., Abacıoğlu, N., Usman?, ?A. G., Taner, N., Şehirli, A. Ö. ve Abba, S. I. (2023). Novel hybridized computational paradigms integrated with five stand-alone algorithms for clinical prediction of HCV status among patients: A data-driven technique. Life-Basel, 13(1). https://doi.org/10.3390/life13010079
dc.identifier.doi10.3390/life13010079
dc.identifier.issn2075-1729
dc.identifier.issue1
dc.identifier.pmid36676028
dc.identifier.scopus2-s2.0-85147818507
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/life13010079
dc.identifier.urihttps://hdl.handle.net/20.500.12511/10462
dc.identifier.volume13
dc.identifier.wos000918146000001en_US
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorTaner, Neda
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofLife-Baselen_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.subjectHepatitis C Status
dc.subjectMachine Learning
dc.subjectArtificial Intelligence
dc.subjectClinical Variables
dc.subjectHybrid Paradigms
dc.titleNovel hybridized computational paradigms integrated with five stand-alone algorithms for clinical prediction of HCV status among patients: A data-driven technique
dc.typeArticle

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