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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.09027 |
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| _version_ | 1866909655345659904 |
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| author | Chattopadhyay, Souradeep Basulto-Elias, Guillermo Chang, Jun Ha Rizzo, Matthew Hallmark, Shauna Sharma, Anuj Sarkar, Soumik |
| author_facet | Chattopadhyay, Souradeep Basulto-Elias, Guillermo Chang, Jun Ha Rizzo, Matthew Hallmark, Shauna Sharma, Anuj Sarkar, Soumik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09027 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Mild Cognitive Impairment Using Naturalistic Driving and Trip Destination Modeling Chattopadhyay, Souradeep Basulto-Elias, Guillermo Chang, Jun Ha Rizzo, Matthew Hallmark, Shauna Sharma, Anuj Sarkar, Soumik Machine Learning 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. |
| title | Predicting Mild Cognitive Impairment Using Naturalistic Driving and Trip Destination Modeling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.09027 |