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Early detection of mild cognitive impairment through neuropsychological tests in population screenings: a decision support system integrating ontologies and machine learning

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dc.contributor.authorGómez-Valades Batanero, Alba
dc.contributor.authorMartínez Tomás, Rafael
dc.contributor.authorGarcía Herranz, Sara
dc.contributor.authorBjørnerud, Atle
dc.contributor.authorRincón Zamorano, Mariano
dc.date.accessioned2024-12-09T11:27:31Z
dc.date.available2024-12-09T11:27:31Z
dc.date.issued2024-10-16
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en Frontiers in Neuroinformatics 18:1378281, está disponible en línea en el sitio web del editor: https://doi.org/10.3389/fninf.2024.1378281. The copyrighted version of this article, first published in Frontiers in Neuroinformatics 18:1378281, is available online at the publisher's website: https://doi.org/10.3389/fninf.2024.1378281.
dc.description.abstractMachine learning (ML) methodologies for detecting Mild Cognitive Impairment (MCI) are progressively gaining prevalence to manage the vast volume of processed information. Nevertheless, the black-box nature of ML algorithms and the heterogeneity within the data may result in varied interpretations across distinct studies. To avoid this, in this proposal, we present the design of a decision support system that integrates a machine learning model represented using the Semantic Web Rule Language (SWRL) in an ontology with specialized knowledge in neuropsychological tests, the NIO ontology. The system’s ability to detect MCI subjects was evaluated on a database of 520 neuropsychological assessments conducted in Spanish and compared with other well-established ML methods. Using the F2 coefficient to minimize false negatives, results indicate that the system performs similarly to other well-established ML methods (F2TE2=0.830, only below bagging, F2BAG=0.832) while exhibiting other significant attributes such as explanation capability and data standardization to a common framework thanks to the ontological part. On the other hand, the system’s versatility and ease of use were demonstrated with three additional use cases: evaluation of new cases even if the acquisition stage is incomplete (the case records have missing values), incorporation of a new database into the integrated system, and use of the ontology capabilities to relate different domains. This makes it a useful tool to support physicians and neuropsychologists in population-based screenings for early detection of MCI.en
dc.description.versionversión publicada
dc.identifier.citationGómez-Valadés A, Martínez-Tomás R, García-Herranz S, Bjørnerud A and Rincón M (2024) Early detection of mild cognitive impairment through neuropsychological tests in population screenings: a decision support system integrating ontologies and machine learning. Front. Neuroinform. 18:1378281. doi:https://doi.org/10.3389/fninf.2024.1378281
dc.identifier.doihttps://doi.org/10.3389/fninf.2024.1378281
dc.identifier.issn1662-5196
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24755
dc.journal.titleFrontiers in Neuroinformatics
dc.journal.volume18
dc.language.isoen
dc.publisherFrontiers Media
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.subject.keywordsontologyen
dc.subject.keywordsmachine learningen
dc.subject.keywordsSWRLen
dc.subject.keywordsdecision treeen
dc.subject.keywordsensembleen
dc.subject.keywordsdecision support systemen
dc.subject.keywordsMCIen
dc.titleEarly detection of mild cognitive impairment through neuropsychological tests in population screenings: a decision support system integrating ontologies and machine learningen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscovery05104596-1880-4adc-8bfd-cf92aa718ee0
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