Classification of Drivers with HIV-Associated Neurocognitive Disorders using Virtual Driving Test Performance Data
Keywords:Driving Simulator, HIV-Associated Neurocognitive Disorder, Feature Selection, Ensemble Classifiers, Cognitive Screening
In this work we focus on the problem of identifying drivers with neurocognitive impairment (NCI), specifically an NCI specific to people with HIV (PWH) called HIV-associated neurocognitive disorders (HAND) directly from driving simulator data. Since NCI-screening is typically only effective for more progressed forms of HAND, there is a critical need to identify individuals that should be referred to specialists in order to mitigate potentially dangerous driving behaviors and improve their quality of life. Data collected from (n = 81) study participants that used the virtual driving test (VDT) platform were analyzed in order to predict which drivers had NCI. Of the (n = 62) PWH participants recruited, (n = 35) had HAND; of the remaining (n = 19) HIV negative participants, (n = 7) had non-HAND NCI (e.g., Parkinson’s Disease, Alzheimer’s, etc.). In three separate experiments, subsets of VDT data were first selected via Kruskal-Wallis feature ranking and then used as ensemble inputs to classify whether or not drivers had NCI. Within the PWH population, HAND could be classified with 69.4% accuracy and a risk ratio of 2.09 (95% CI 1.52, 2.65); within the HIV negative population, non-HAND NCI could be classified with 84.2% accuracy, risk ratio of 8.25 (6.34, 10.16); and within the combined population, NCI (regardless of causation) could be classified with 63.0% accuracy, risk ratio of 1.67 (1.22, 2.11).
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Copyright (c) 2023 David Grethlein, Venk Kandadai, Will Dampier
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