Enhancing Dementia Risk Prediction with Heart Rate and Machine Learning in the Canadian Longitudinal Study on Aging (CLSA)

The addition of resting heart rate significantly improves the predictive accuracy of the CAIDE model, particularly when using machine learning methods. This highlights the potential of this approach to enhance traditional risk models, enabling more precise dementia risk assessments, closer monitoring, and the implementation of preventive strategies.