Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
INTRODUCTION
Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used machine learning to re-examine the diagnostic potential of clinical measures for dementia.
METHODS
Data was sourced from the Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing (AIBL) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Clinical variables included 21 measures across medical history, hematological and other blood tests, and APOE genotype. Tree-based machine learning algorithms and artificial neural networks were used.
RESULTS
APOE genotype was the best predictor of dementia cases and healthy controls. Our results, however, demonstrated that there are limitations when using publicly accessible cohort data that may limit the generalizability and interpretability of such predictive models.
DISCUSSION
Future research should examine the use of routine APOE genetic testing for dementia diagnostics. It should also focus on clearly unifying data across clinical cohorts.
