Multi-modal machine learning for predicting amyloid positivity using on-ramp driving.

Early detection of amyloid p is critical for Alzheimer's disease (AD) risk identification. This study leverages machine learning of multi-modal attributes, including vehicular, physiological, and demographic data, to classify older adults with and without amyloid positivity. Driving data and physiological responses from 53 cognitively normal older drivers with known positron emission tomography amyloid status were collected during freeway on-ramp, merging, and post-merge stages of a fixed-course drive. Statistically significant features (P ≤ 0.05) were used to train random forest and XGBoost classifiers to classify amyloid-positive and -negative participants, with feature importance evaluated based on model performance. Integrating multiple data modalities (demographics, vehicular, and physiological features) improved classification performance, distinguishing amyloid status. XGBoost with all statistically significant features achieved the highest accuracy (85.1%). Vehicular data provided the most predictive power, highlighting driving behavior relevance for classification. Results underscore the importance of complementary insights from on-ramp multi-modal data to predict amyloid status and potential early AD detection. We analyzed driving behavior and physiological signals for cognitive decline detection.Artificial intelligence (AI) models (random forest, XGBoost) effectively classified amyloid beta positive and negative participants.Interpretable AI identified on-ramp driving, that is, ZOI_1, as key for classification.Multi-modal analysis during on-ramp driving aids early cognitive decline detection.Challenging traffic environments enable non-invasive cognitive health monitoring.