# Predicting Mild Cognitive Impairment Using Naturalistic Driving and Trip Destination Modeling

arXiv: https://arxiv.org/abs/2504.09027 · HTML: https://mlsanity.com/paper/2504.09027/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 12 Apr 2025.

## Abstract (arXiv)

Understanding the relationship between mild cognitive impairment (MCI) and driving behavior is essential for enhancing road safety, particularly among older adults. This study introduces a novel approach by incorporating specific trip destinations-such as home, work, medical appointments, social activities, and errands-using geohashing to analyze the driving habits of older drivers in Nebraska. We employed a two-fold methodology that combines data visualization with advanced machine learning models, including C5.0, Random Forest, and Support Vector Machines, to assess the effectiveness of these location-based variables in predicting cognitive impairment. Notably, the C5.0 model showed a robust and stable performance, achieving a median recall of 0.68, which indicates that our methodology accurately identifies cognitive impairment in drivers 68% of the time. This emphasizes our model's capacity to reduce false negatives, a crucial factor given the profound implications of failing to identify impaired drivers. Our findings underscore the innovative use of life-space variables in understanding and predicting cognitive decline, offering avenues for early intervention and tailored support for affected individuals.