Enhancing dementia risk prediction with heart rate and machine learning in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".