Alzheimer’s disease (AD) is the most common type of dementia that affects older adults. Earlier detection of AD is essential for treatment, but current methods like cerebrospinal fluid or PET scans are expensive and not widely available. This study explored the potential of using everyday driving behavior and blood-based biomarkers to identify AD. Data were collected from 142 older adults who drove with a datalogger plugged into their vehicles. A machine learning analysis was used to analyze the data and predict AD biomarker status. The results showed that driving behavior (e.g., evening trips, jerk, hard braking) by itself or combined with age and genetic factors could classify AD biomarker status with moderate to high accuracy. These findings suggest that diagnosing driving behavior and blood-based biomarkers could be a promising approach for the early detection of AD.

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