Enhancing dementia risk prediction with heart rate and machine learning in the Canadian Longitudinal Study on Aging

BackgroundAccurate and accessible risk assessment tools are essential for effective dementia management. The Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) model is the widely used tool to assess mid-life dementia risk.ObjectiveTo determine whether adding resting heart rate (RHR), a simple, readily measurable, non-invasive vital sign, improves dementia risk prediction within the CAIDE model using machine learning methods.MethodsData from 27,768 participants of comprehensive cohort in the Canadian Longitudinal Study on Aging were analyzed to predict 3-year dementia risk. Predictive models were developed using random forest and support vector machine algorithms. Performance was assessed using key metrics, including area under the receiver operating characteristic curve (AUC), sensitivity, specificity, Matthew's correlation coefficient (MCC), and Brier score. Internal cross-validation was used to ensure model robustness.ResultsAmong the 18,013 participants with complete data for analysis, 516 (2.86%) exhibited dementia. Incorporating RHR into the CAIDE model led to a significant improvement in predictive accuracy. Random forest models with RHR achieved an AUC of 0.67 and an MCC of 0.32 in training data, compared to 0.65 and 0.29 in the test data. Similarly, support vector machines demonstrated a 2-3% increase in both AUC and MCC with the inclusion of RHR.ConclusionsIncorporating RHR modestly but significantly improves the predictive performance of the CAIDE model using machine learning methods. This approach may support earlier identification of at-risk individuals using non-invasive, routinely available data, representing a step toward scalable and practical dementia risk screening in clinical and community settings.