Using ensemble learning techniques and longitudinal data from a large naturalistic driving study, researchers at Columbia University Mailman School of Public Health, Fu Foundation School of Engineering and Applied Science, and Vagelos College of Physicians and Surgeons have developed a novel, interpretable, and highly accurate algorithm for predicting mild cognitive impairment and dementia in older drivers. Digital markers refer to variables generated from data captured through recording devices in real-world settings. These data could be processed to measure driving behavior, performance, and tempo-spatial pattern in exceptional detail. The study is published in the journal Artificial Intelligence in Medicine.

The researchers used an interaction-based classification method for selecting predictive variables in the dataset. This learning model has achieved an accuracy of 96 percent in predicting mild cognitive impairment and dementia, outperforming traditional machine learning models such as logistic regression and random forests—a statistical technique widely used in AI for classi