A Study on Psychometric Assessment Data for Autonomous Dementia Detection

Chloe Barnes

doi:10.1145/3529190.3534726

A Study on Psychometric Assessment Data for Autonomous Dementia Detection

Chloe Barnes^*

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Published conference output › Conference 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 language	English
Title of host publication	Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2022
Publisher	ACM
Pages	383-389
Number of pages	7
ISBN (Electronic)	9781450396318
DOIs	https://doi.org/10.1145/3529190.3534726
Publication status	Published - 29 Jun 2022
Event	15th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2022 - Corfu, Greece Duration: 29 Jun 2022 → 1 Jul 2022

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	15th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2022
Country/Territory	Greece
City	Corfu
Period	29/06/22 → 1/07/22

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

Access to Document

10.1145/3529190.3534726Licence: CC BY 4.0

Barnes2022_VoR
© 2022 Copyright held by the owner/author(s).
Final published version, 4.79 MBLicence: CC BY 4.0

Cite this

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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. ",

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Barnes, C 2022, A Study on Psychometric Assessment Data for Autonomous Dementia Detection. in Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2022. ACM International Conference Proceeding Series, ACM, pp. 383-389, 15th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2022, Corfu, Greece, 29/06/22. https://doi.org/10.1145/3529190.3534726

A Study on Psychometric Assessment Data for Autonomous Dementia Detection. / Barnes, Chloe.

Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2022. ACM, 2022. p. 383-389 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Published conference output › Conference publication

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A Study on Psychometric Assessment Data for Autonomous Dementia Detection

Abstract

Publication series

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Bibliographical note

Keywords

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