Skip to main content

ORIGINAL RESEARCH article

Front. Neuroinform.
Volume 18 - 2024 | doi: 10.3389/fninf.2024.1378281
This article is part of the Research Topic Application of Machine Learning in the Diagnosis of Dementia - Volume II View all 6 articles

Early Detection of Mild Cognitive Impairment through Neuropsychological Tests in Population Screenings: A Decision Support System Integrating Ontologies and Machine Learning

Provisionally accepted
Alba Gómez-Valadés Alba Gómez-Valadés 1*Rafael Martínez-Tomás Rafael Martínez-Tomás 1Sara García-Herranz Sara García-Herranz 1Atle Bjørnerud Atle Bjørnerud 2Mariano Rincon Mariano Rincon 1
  • 1 National University of Distance Education (UNED), Madrid, Spain
  • 2 Oslo University Hospital, Oslo, Nordland, Norway

The final, formatted version of the article will be published soon.

    Machine 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. Con formato: Inglés (Estados Unidos) Código de campo cambiado Con formato: Sin espaciado

    Keywords: ontology, machine learning, SWRL, decision tree, ensemble, decision support system, MCI

    Received: 29 Jan 2024; Accepted: 04 Oct 2024.

    Copyright: © 2024 Gómez-Valadés, Martínez-Tomás, García-Herranz, Bjørnerud and Rincon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Alba Gómez-Valadés, National University of Distance Education (UNED), Madrid, Spain

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.