Open AccessMachine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry
Author(s) -
Albright Jack,
Ashford Miriam T.,
Jin Chengshi,
Neuhaus John,
Rabinovici Gil D.,
Truran Diana,
Maruff Paul,
Mackin R. Scott,
Nosheny Rachel L.,
Weiner Michael W.
Publication year - 2021
Publication title -
alzheimer's and dementia: diagnosis, assessment and disease monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.497
H-Index - 37
ISSN - 2352-8729
DOI - 10.1002/dad2.12207
Subject(s) - machine learning , artificial intelligence , inclusion (mineral) , computer science , feature selection , cognition , psychology , psychiatry , social psychology
This study investigated the extent to which subjective and objective data from an online registry can be analyzed using machine learning methodologies to predict the current brain amyloid beta (Aβ) status of registry participants. Methods We developed and optimized machine learning models using data from up to 664 registry participants. Models were assessed on their ability to predict Aβ positivity using the results of positron emission tomography as ground truth. Results Study partner–assessed Everyday Cognition score was preferentially selected for inclusion in the models by a feature selection algorithm during optimization. Discussion Our results suggest that inclusion of study partner assessments would increase the ability of machine learning models to predict Aβ positivity.