Binary Classification Using Neural and Clinical Features: An Application in Fibromyalgia With Likelihood-Based Decision Level Fusion

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dc.contributor.authorGökçay, Didem
dc.contributor.authorEken, Aykut
dc.contributor.authorBaltacı, Serdar
dc.date.accessioned2020-04-30T22:40:11Z
dc.date.available2020-04-30T22:40:11Z
dc.date.issued2019
dc.departmentDÜ, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümüen_US
dc.descriptionEken, Aykut/0000-0002-7023-7930; Gokcay, Didem/0000-0002-1101-0306en_US
dc.descriptionWOS: 000474575600015en_US
dc.descriptionPubMed: 29994341en_US
dc.description.abstractAmong several features used for clinical binary classification, behavioral performance, questionnaire scores, test results, and physical exam reports can be counted. Attempts to include neuroimaging findings to support clinical diagnosis are scarce due to difficulties in collecting such data, as well as problems in integration of neuroimaging findings with other features. The binary classification method proposed here aims to merge small samples from multiple sites so that a large cohort, which better describes the features of the disease can be built. We implemented a simple and robust framework for detection of fibromyalgia, using likelihood during decision level fusion. This framework supports sharing of classifier applications across clinical sites and arrives at a decision by fusing results from multiple classifiers. If there are missing opinions from some classifiers due to inability to collect their input features, such degradation in information is tolerated. We implemented this method using functional near infrared spectroscopy (fNIRS) data collected from fibromyalgia patients across three different tasks. Functional connectivity maps are derived from these tasks as features. In addition, self-reported clinical features are also used. Five classifiers are trained using k nearest neighborhood (kNN), linear discriminant analysis (LDA), and support vector machine (SVM). Fusion of classification opinions from multiple classifiers based on likelihood ratios outperformed individual classifier performances. When 2, 3, 4, and 5 different classifiers are fused, sensitivity, and specificity figures of 100% could be obtained based on the choice of the classifier set.en_US
dc.identifier.doi10.1109/JBHI.2018.2844300en_US
dc.identifier.endpage1498en_US
dc.identifier.issn2168-2194
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1490en_US
dc.identifier.urihttps://doi.org/10.1109/JBHI.2018.2844300
dc.identifier.urihttps://hdl.handle.net/20.500.12684/2924
dc.identifier.volume23en_US
dc.identifier.wosWOS:000474575600015en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Journal Of Biomedical And Health Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClinical binary classificationen_US
dc.subjectdecision level fusionen_US
dc.subjectfibromyalgiaen_US
dc.subjectfunctional connectivityen_US
dc.subjectfunctional near infrared spectroscopy (fNIRS)en_US
dc.subjectlikelihooden_US
dc.titleBinary Classification Using Neural and Clinical Features: An Application in Fibromyalgia With Likelihood-Based Decision Level Fusionen_US
dc.typeArticleen_US

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