Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry. Issue 1 (9th June 2021)

• Record Type: Journal Article
• Authors: 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.
• Is Part Of: Alzheimer's & dementia. Volume 13:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-09
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer's disease -- Diagnosis -- Periodicals; Dementia -- Periodicals; Dementia -- Diagnosis -- Periodicals; 616.831
• DOI: 10.1002/dad2.12207
• Languages: English
• ISSNs: 2352-8729

Abstract

Abstract: Introduction: 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.