@article{ref1,
title="Machine learning approach to predict on-road driving ability in healthy older people",
journal="Psychiatry and the Clinical Neurosciences",
year="2020",
author="Yamamoto, Yasuharu and Hirano, Jinichi and Yoshitake, Hiroshi and Negishi, Kazuno and Mimura, Masaru and Shino, Motoki and Yamagata, Bun",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="AIM: In Japan, fatal traffic accidents due to older drivers are on the rise. Considering that approximately half the older drivers who caused fatal accidents were 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.   METHODS: We enrolled thirty-three 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 visual acuity test.   RESULTS: The linear SVM 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 maximum visual acuity, were consistently selected as essential features for the best classification model.   CONCLUSION: 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. This article is protected by copyright. All rights reserved.<p /> <p>Language: en</p>",
language="en",
issn="1323-1316",
doi="10.1111/pcn.13084",
url="http://dx.doi.org/10.1111/pcn.13084"
}