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Discriminating patients with paediatric idiopathic hyperhidrosis from healthy subjects with infrared thermography and machine learning classifiers

dc.contributor.authorRicardo Vardasca, PhD, ASIS, FRPS
dc.date.accessioned2021-12-29T14:19:47Z
dc.date.available2021-12-29T14:19:47Z
dc.date.issued2020
dc.description.abstractIdiopathic hyperhidrosis (IH) is a medical condition characterised by excessive sweating beyond what is physiologically necessary for thermoregulation affecting mainly the axillae and palms. It affects seriously the quality of life of patients and has an incidence at paediatric age of 1.6%. The diagnosis is subjective relying only the the patient claim and physician perception. It is aim of this research to evaluate if dynamic infrared thermography (IRT) along with machine learning classifier on the thermal data are able to discriminate IH paediatric patients from healthy subjects. Using dynamic IRT, through convective provocation, on the views of axillae and palms, it was possible to discriminate IH paediatric patients from healthy subjects using artificial neural networks (ANN) and random forests (RF) in thermal measurements with high accuracy (>99%), the same was not possible only with the thermal data and statistics.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.21611/qirt.2020.154pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.26/38505
dc.language.isoengpt_PT
dc.titleDiscriminating patients with paediatric idiopathic hyperhidrosis from healthy subjects with infrared thermography and machine learning classifierspt_PT
dc.typeconference object
dspace.entity.typePublication
person.familyNameVardasca
person.givenNameRicardo
person.identifierR-001-FFR
person.identifier.ciencia-id9F17-FD5F-E767
person.identifier.orcid0000-0003-4217-2882
person.identifier.ridJ-4948-2013
person.identifier.scopus-author-id24491279800
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication33602b11-6c79-40f9-a768-d7c792bc2d57
relation.isAuthorOfPublication.latestForDiscovery33602b11-6c79-40f9-a768-d7c792bc2d57

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