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Updated: May 10, 2025
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
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Quad-tree Based Driver Classification using Deep Learning for Mild Cognitive Impairment Detection
Seyedeh Gol Ara Ghoreishi1, Charles Boateng1, Sonia Moshfeghi1
1Florida Atlantic University, Boca Raton, USA.
IEEE Access : Practical Innovations, Open Solutions
|April 21, 2025
Summary
This study introduces a novel quad-tree approach for driver classification to detect Mild Cognitive Impairment (MCI). The method effectively analyzes driving patterns, achieving high accuracy for improved road safety and cognitive health monitoring.
Area of Science:
- Computational neuroscience
- Transportation engineering
- Machine learning for healthcare
Background:
- Detecting Mild Cognitive Impairment (MCI) is crucial for timely intervention and patient care.
- Analyzing driving patterns offers a non-invasive method for cognitive health assessment.
- Existing driver classification methods face challenges with large, complex GPS trajectory data.
Purpose of the Study:
- To develop an effective method for classifying drivers with Mild Cognitive Impairment (MCI) using GPS data.
- To propose a novel geo-regional quad-tree structure for analyzing spatial driving patterns.
- To enhance driver classification accuracy through advanced feature representation and deep learning.
Main Methods:
- Utilized a real-world dataset of GPS points on a transportation network.
- Developed a geo-regional quad-tree structure to represent the spatial hierarchy of driving trajectories.
- Engineered new driving features for input into a Convolutional Neural Network (CNN).
- Implemented a quad-tree based driver classification (QBDC) algorithm.
Main Results:
- The proposed quad-tree based driver classification (QBDC) algorithm achieved a 95% F1 score.
- Demonstrated significant performance improvement over baseline models.
- Validated the effectiveness of geo-regional quad-trees in extracting interpretable features from driving patterns.
Conclusions:
- Geo-regional quad-tree structures are effective for describing complex driving patterns and classifying drivers.
- The proposed approach shows significant potential for improving road safety and cognitive health monitoring.
- This method offers a promising avenue for early detection of cognitive decline through driving behavior analysis.
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