Over the course of the LongROAD study, 33 subjects were diagnosed with MCI and 31 with dementia. A series of machine learning models were trained on the LongROAD data, tasked with detecting MCI and dementia from driving behaviors. “Based on variables derived from the naturalistic driving data and basic demographic characteristics, such as age, sex, race/ethnicity and education level, we could predict mild cognitive impairment and dementia with 88 percent accuracy,” says Sharon Di, lead author on the new study. Although age was the number one factor for detecting MCI or dementia, a number of driving variables closely followed. These include, “the percentage of trips traveled within 15 miles (24 km) of home … the length of trips starting and ending at home, minutes per trip, and number of hard braking events with deceleration rates 0.35 g.” Using driving variables alone, the models could still predict those MCI or dementia drivers with 66 percent accuracy. The new study was published in the journal Geriatrics.
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