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  5. Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) - ΙΑΤ
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Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/18528
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dc.contributor.authorKelly, J. G.en
dc.contributor.authorAngelov, P. P.en
dc.contributor.authorTrevisan, J.en
dc.contributor.authorVlachopoulou, A.en
dc.contributor.authorParaskevaidis, E.en
dc.contributor.authorMartin-Hirsch, P. L.en
dc.contributor.authorMartin, F. L.en
dc.date.accessioned2015-11-24T18:53:16Z-
dc.date.available2015-11-24T18:53:16Z-
dc.identifier.issn1618-2650-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/18528-
dc.rightsDefault Licence-
dc.subject*Algorithmsen
dc.subjectFemaleen
dc.subjectHumansen
dc.subject*Neoplasm Staging/instrumentation/methodsen
dc.subjectPredictive Value of Testsen
dc.subjectSpectroscopy, Fourier Transform Infrareden
dc.subjectUterine Cervical Neoplasms/*pathology/physiopathologyen
dc.titleRobust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClassen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primary10.1007/s00216-010-4179-5-
heal.identifier.secondaryhttp://www.ncbi.nlm.nih.gov/pubmed/20857283-
heal.identifier.secondaryhttp://www.springerlink.com/content/121451423505hn27/fulltext.pdf-
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικήςel
heal.publicationDate2010-
heal.abstractAlthough the UK cervical screening programme has reduced mortality associated with invasive disease, advancement from a high-throughput predictive methodology that is cost-effective and robust could greatly support the current system. We combined analysis by attenuated total reflection Fourier-transform infrared spectroscopy of cervical cytology with self-learning classifier eClass. This predictive algorithm can cope with vast amounts of multidimensional data with variable characteristics. Using a characterised dataset [set A: consisting of UK cervical specimens designated as normal (n = 60), low-grade (n = 60) or high-grade (n = 60)] and one further dataset (set B) consisting of n = 30 low-grade samples, we set out to determine whether this approach could be robustly predictive. Variously extending the training set consisting of set A with set B data produced good classification rates with three two-class cascade classifiers. However, a single three-class classifier was equally efficient, producing a user-friendly, applicable methodology with improved interpretability (i.e., better classification with only one set of fuzzy rules). As data from set B were added incrementally to the training set, the model learned and evolved. Additionally, monitoring of results of the set B low-grade specimens (known to be low-grade cervical cytology specimens) provided the opportunity to explore the possibility of distinguishing patients likely to progress towards invasive disease. eClass exhibited a remarkably robust predictive power in a user-friendly fashion (i.e., high throughput, ease of use) compared to other classifiers (k-nearest neighbours, support vector machines, artificial neural networks). Development of eClass to classify such datasets for applications such as screening exhibits robustness in identifying a dichotomous marker of invasive disease progression.en
heal.journalNameAnal Bioanal Chemen
heal.journalTypepeer-reviewed-
heal.fullTextAvailabilityTRUE-
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