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

Topic: Risk, prevention and care in population studies

Among the 18,013 participants with complete data for analysis, 516 (2.86%) exhibited cognitive impairment. 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. 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.

@article{alaka2025-41434961,
  author  = {Alaka, Shakiru A. and Ngan, SoFong Cam and MacPherson, Rebecca E. K. and Pickett, William and Chen, Christopher and Sze, Newman},
  title   = {{Enhancing Dementia Risk Prediction with Heart Rate and Machine Learning in the Canadian Longitudinal Study on Aging (CLSA)}},
  journal = {Alzheimer s \& Dementia},
  year    = {2025},
  doi     = {10.1002/alz70860_098393},
  pmid    = {41434961},
  url     = {https://doi.org/10.1002/alz70860_098393}
}

Key findings

• Among the 18,013 participants with complete data for analysis, 516 (2.86%) exhibited cognitive impairment.
• 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.
• The addition of resting heart rate significantly improves the predictive accuracy of the CAIDE model, particularly when using machine learning methods.

Why it matters

Adding resting heart rate to the CAIDE model improved predictive accuracy for cognitive impairment, with random forest models reaching an AUC of 0.67 in training and 0.65 in test data. This is about improving risk prediction and monitoring, not treating, curing or preventing dementia.

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Study details

• Study designlongitudinal study
• Participants27,768
• Effect size2.86%
• Evidence strengthModerate

Read the original

• DOI: 10.1002/alz70860_098393
• PubMed: 41434961
• Original source →
• Free full text: Alzheimer s & Dementia →

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