A Study on Psychometric Assessment Data for Autonomous Dementia Detection

Chloe Barnes*

*Corresponding author for this work

    Research output: Chapter in Book/Published conference outputConference publication

    Abstract

    Dementia and Alzheimer's disease are characterised by cognitive decline, and diagnoses are predicted to rise due to an ageing population. Psychometric assessments are widely used by clinicians to inform the diagnosis of dementia, however these may not be as accurate or accessible for patients with lower levels of literacy or socioeconomic status. This study explores how machine learning models can detect dementia when trained on combinations of attributes from multi-modal datasets containing psychometric and Magnetic Resonance Imaging (MRI) data. When psychometric testing is not available, results show that the Random Forest classifier achieves a balanced accuracy, sensitivity and specificity of 84.75%, 79.10%, and 90.41% respectively before the dataset was standardised, and 84.34%, 78.27%, and 90.41% after - outperforming identical models trained on data from a single psychometric test.

    Original languageEnglish
    Title of host publicationProceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2022
    PublisherACM
    Pages383-389
    Number of pages7
    ISBN (Electronic)9781450396318
    DOIs
    Publication statusPublished - 29 Jun 2022
    Event15th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2022 - Corfu, Greece
    Duration: 29 Jun 20221 Jul 2022

    Publication series

    NameACM International Conference Proceeding Series

    Conference

    Conference15th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2022
    Country/TerritoryGreece
    CityCorfu
    Period29/06/221/07/22

    Bibliographical note

    © 2022 Copyright held by the owner/author(s).

    Funding Information:
    The dataset used in this study (Open Access Series of Imaging Studies (OASIS) [12]): is publicly available at http://www.oasis-brains.org; has Principal Investigators D. Marcus, R, Buckner, J. Csernansky, and J. Morris; and is supported by grants P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, and U24 RR021382.

    Keywords

    • Dementia Classification
    • Machine Learning
    • Multi-Modal Data

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