Machine-learning approach to predict on-road driving ability in healthy older people.
aged
automobile driving
distracted driving
machine learning
support vector machine
Journal
Psychiatry and clinical neurosciences
ISSN: 1440-1819
Titre abrégé: Psychiatry Clin Neurosci
Pays: Australia
ID NLM: 9513551
Informations de publication
Date de publication:
Sep 2020
Sep 2020
Historique:
received:
19
02
2020
revised:
27
05
2020
accepted:
08
06
2020
pubmed:
15
6
2020
medline:
16
10
2021
entrez:
15
6
2020
Statut:
ppublish
Résumé
In Japan, fatal traffic accidents due to older drivers are on the rise. Considering that approximately half the older drivers who have caused fatal accidents are cognitively normal healthy people, it has been required to detect older drivers who are cognitively normal but at high risk of having fatal traffic accidents. However, a standardized method for assessing the driving ability of older drivers has not yet been established. We thus aimed to identify a new sensing method for the evaluation of the on-road driving ability of healthy older people on the basis of vehicle behaviors. We enrolled 33 healthy older individuals aged over 65 years and utilized a machine-learning approach to dissociate unsafe drivers from safe drivers based on cognitive assessments and a functional visual acuity test. The linear support vector machine classifier successfully dissociated unsafe drivers from safe drivers with accuracy of 84.8% (sensitivity of 66.7% and specificity of 95.2%). Five clinical parameters, namely age, the first trial of the Rey Auditory Verbal Learning Test immediate recall, the delayed recall of the Rey-Osterrieth Complex Figure Test, the result of the free-drawn Clock Drawing Test, and maximal visual acuity, were consistently selected as essential features for the best classification model. Our findings improve our understanding of clinical risk factors leading to unsafe driving and may provide insight into a new intervention that prevents fatal traffic accidents caused by healthy older people.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
488-495Subventions
Organisme : JSPS KAKENHI
ID : JP16H03130
Organisme : Research and development of technology for enhancing functional recovery of elderly and disabled people based on non-invasive brain imaging and robotic assistive devices
Organisme : The General Insurance Association of Japan
Organisme : The Mitsui Sumitomo Insurance Welfare Foundation
Organisme : Mitsui Sumitomo Insurance Welfare Foundation
Organisme : Japan Society for the Promotion of Science KAKENHI
Organisme : National Institute of Information and Communications Technology
Organisme : General Insurance Association of Japan
Informations de copyright
© 2020 The Authors Psychiatry and Clinical Neurosciences © 2020 Japanese Society of Psychiatry and Neurology.
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