HClass: automatic classification tool for health pathologies using artificial intelligence techniques

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dc.contributor.authorGarcía Chimeno, Yolanda
dc.contributor.authorGarcía-Zapirain, Begoña
dc.date.accessioned2026-02-26T13:34:35Z
dc.date.available2026-02-26T13:34:35Z
dc.date.issued2015-02-01
dc.date.updated2026-02-26T13:34:35Z
dc.description.abstractRhe classification of subjects' pathologies enables a rigorousness to be applied to the treatment of certain pathologies, as doctors on occasions play with so many variables that they can end up confusing some illnesses with others. Thanks to Machine Learning techniques applied to a health-record database, it is possible to make using our algorithm. hClass contains a non-linear classification of either a supervised, non-supervised or semi-supervised type. The machine is configured using other techniques such as validation of the set to be classified (cross-validation), reduction in features (PCA) and committees for assessing the various classifiers. The tool is easy to use, and the sample matrix and features that one wishes to classify, the number of iterations and the subjects who are going to be used to train the machine all need to be introduced as inputs. As a result, the success rate is shown either via a classifier or via a committee if one has been formed. A 90% success rate is obtained in the ADABoost classifier and 89.7% in the case of a committee (comprising three classifiers) when PCA is applied. This tool can be expanded to allow the user to totally characterise the classifiers by adjusting them to each classification use.en
dc.identifier.citationGarcia-Chimeno, Y., & Garcia-Zapirain, B. (2015). HClass: automatic classification tool for health pathologies using artificial intelligence techniques. Bio-Medical Materials and Engineering, 26, S1821-S1828. https://doi.org/10.3233/BME-151484
dc.identifier.doi10.3233/BME-151484
dc.identifier.eissn1878-3619
dc.identifier.issn0959-2989
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5263
dc.language.isoeng
dc.publisherIOS Press
dc.rights© 2015 – IOS Press and the authors
dc.subject.otherClassification
dc.subject.otherCommittee
dc.subject.otherCross-validation
dc.subject.otherMachine learning
dc.subject.otherPCA
dc.titleHClass: automatic classification tool for health pathologies using artificial intelligence techniquesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPageS1828
oaire.citation.startPageS1821
oaire.citation.titleBio-Medical Materials and Engineering
oaire.citation.volume26
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc/4.0/
oaire.versionVoR
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