Predicting future self-reported motor vehicle collisions in subjects with primary open-angle glaucoma using the penalized support vector machine method

Kenya Yuki, Ryo Asaoka, Sachiko Awano-Tanabe, Takeshi Ono, Daisuke Shiba, Hiroshi Murata, Kazuo Tsubota

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Purpose: We predict the likelihood of a future motor vehicle collision (MVC) from visual function data, attitudes to driving, and past MVC history using the penalized support vector machine (pSVM) in subjects with primary open-angle glaucoma (POAG). Methods: Patients with POAG were screened prospectively for eligibility and 185 were analyzed in this study. Self-reported MVCs of all participants were recorded for 3 years from the baseline using a survey questionnaire every 12 months. A binocular integrated visual field (IVF) was calculated for each patient by merging a patient’s monocular Humphrey Field Analyzer (HFA) visual fields (VFs). The IVF was divided into six regions, based on eccentricity and the right or left hemifield, and the average of the total deviation (TD) values in each of these six areas was calculated. Then, the future MVCs were predicted using various variables, including age, sex, 63 variables of 52 TD values, mean of the TD values, visual acuities (VAs), six sector average TDs with (predpenSVM_all) and without (predpenSVM_basic) the attitudes in driving, and also past MVC history, using the pSVM method, applying the leave-one-out cross validation. Results: The relationship between predpenSVM_basic and the future MVC approached significance (odds ratio = 1.15, [0.99–1.29], P = 0.064, logistic regression). A significant relationship was observed between predpenSVM_all and the future MVC (odds ratio = 1.21, P = 0.0015). Conclusions: It was useful to predict future MVCs in patients with POAG using visual function metrics, patients’ attitudes to driving, and past MVC history, using the pSVM. Translational Relevance: Careful consideration is needed when predicting future MVCs in POAG patients using visual function, and without driving attitude and MVC history.

Original languageEnglish
Article number14
JournalTranslational Vision Science and Technology
Volume6
Issue number3
DOIs
Publication statusPublished - 2017 May

Keywords

  • Glaucoma
  • Motor vehicle collision
  • Support vector machine
  • Visual field

ASJC Scopus subject areas

  • Biomedical Engineering
  • Ophthalmology

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