Application of machine learning techniques for dementia severity prediction from psychometric tests in the elderly population

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datacite.alternateIdentifier.citationAPPLIED NEUROPSYCHOLOGY-ADULT,Vol.,,2022
datacite.alternateIdentifier.doi10.1080/23279095.2022.2162899
datacite.creatorCalderon, Carlos
datacite.creatorBekios Calfa, Juan
datacite.creatorBekios Canales, Nikolas
datacite.creatorVeliz Garcia, Oscar
datacite.creatorBeyle, Christian
datacite.creatorPalominos, Diego
datacite.creatorAvalos Tejeda, Marcelo
datacite.creatorDomic Siede, Marcos
datacite.date2022
datacite.subject.englishDementia
datacite.subject.englishdiagnosis
datacite.subject.englishmachine learning
datacite.subject.englishpsychometry
datacite.subject.englishcognitive impairment
datacite.titleApplication of machine learning techniques for dementia severity prediction from psychometric tests in the elderly population
dc.date.accessioned2023-06-08T15:48:07Z
dc.date.available2023-06-08T15:48:07Z
dc.description.abstractPrevious research has shown the benefits of early detection and treatment of dementia. This detection is usually performed manually by one or more clinicians based on reports and psychometric testing. Machine learning algorithms provide an alternative method of prediction that may contribute, with an automated process and insights, to the diagnosis and classification of the severity level of dementia. The aim of this study is to explore the use of neuropsychological data from a reduced version of the Addenbrooke's Cognitive Examination III (ACE-III) to predict absence or different levels of dementia severity using the Global Deterioration Scale (GDS) scores through the implementation of the kNN machine learning algorithm. A sample of 1164 elderly people over sixty years old were evaluated using a reduced version of the ACE-III and the GDS. The kNN classifier provided good accuracies using 15 items from the ACE-III and adequately differentiating people with absence and mild impairment, from those with more severe levels of impairment according to the GDS rating. Our results suggest that the kNN algorithm may be used to automate aspects of clinical cognitive impairment classification in the elderly population.
dc.identifier.urihttps://repositoriodigital.uct.cl/handle/10925/5210
dc.language.isoen
dc.publisherROUTLEDGE JOURNALS. TAYLOR & FRANCIS LTD
dc.sourceAPPLIED NEUROPSYCHOLOGY-ADULT
uct.indizacionSCI
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