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. 2021 Jun 9;13(1):e12207.
doi: 10.1002/dad2.12207. eCollection 2021.

Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry

Jack Albright  1 , Miriam T Ashford  2   3 , Chengshi Jin  4 , John Neuhaus  4 , Gil D Rabinovici  5   6 , Diana Truran  2   3 , Paul Maruff  7 , R Scott Mackin  3   8 , Rachel L Nosheny  3   8 , Michael W Weiner  2   3   5   6   8   9
Affiliations

Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry

Jack Albright et al. Alzheimers Dement (Amst). .

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.

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Conflict of interest statement

JA, MTA, CJ, JN, DT, and RLN have no interests to declare. RSM has received grant funding from the National Institute of Mental Health and has received research support from Johnson & Johnson. PM is an employee of Cogstate, Ltd. GDR is Study Chair for the IDEAS study and has received additional research support from National Institutes of Health (NIH), Alzheimer's Association, Tau Consortium, Avid Radiopharmaceuticals, and Eli Lilly. He is a consultant for Axon Neurosciences, General Electric (GE) Healthcare, Eisai, and Merck, and he is an associate editor for JAMA Neurology. MWW receives support for his work from the following funding sources: NIH, Department of Defense, Patient Centered Outcomes Research Institute (PCORI), California Department of Public Health, University of Michigan, Siemens, Biogen, Larry L. Hillblom Foundation, Alzheimer's Association, and the State of California. He also receives support from Johnson & Johnson, Kevin and Connie Shanahan, GE, Vrije Universiteit Medical Center Amsterdam, Australian Catholic University, The Stroke Foundation, and the Veterans Administration. He has served on Advisory Boards for Eli Lilly, Cerecin/Accera, Roche, Alzheon, Inc., Merck Sharp & Dohme Corp., Nestle/Nestec, PCORI, Dolby Family Ventures, National Institute on Aging (NIA), Brain Health Registry, and ADNI. He serves on the editorial boards for Alzheimer's & Dementia, Topics in Magnetic Resonance Imaging, and Magnetic Resonance Imaging. He has provided consulting and/or acted as a speaker/lecturer to Cerecin/Accera, Inc., Alzheimer's Drug Discovery Foundation (ADDF), Merck, BioClinica, Eli Lilly, Indiana University, Howard University, Nestle/Nestec, Roche, Genentech, NIH, Lynch Group GLC, Health & Wellness Partners, Bionest Partners, American Academy of Neurology (AAN), New York University, Japanese Government Alliance, National Center for Geriatrics and Gerontology (Japan), US Against Alzheimer's, Society for Nuclear Medicine and Molecular Imaging (SNMMI), The Buck Institute for Research on Aging, and FUJIFILM‐Toyama Chemical (Japan). He holds stock options with Alzheon, Inc., Alzeca, and Anven.

Figures

FIGURE 1
FIGURE 1
Generation of samples and subsamples
FIGURE 2
FIGURE 2
Performance of models after feature selection. Blue boxes represent baseline performance of models with hyperparameter optimization but no feature selection. Orange boxes represent performance of models with both hyperparameter optimization and feature selection. Abbreviations: RF, random forest; SVM, support vector machine; SP‐ECog, study partner–assessed Everyday Cognition score; CBB, Cogstate Brief Battery score; AUC, area under the receiver‐operating characteristic curve
FIGURE 3
FIGURE 3
Effect of imputation of SP‐ECog and CBB data. Models having significantly different scores with and without imputation (P < .05) are denoted with an asterisk. No imputation was necessary for models without SP‐ECog and CBB scores, but these were built in parallel with rest as controls; minor differences between paired results for these models are the result of random variation inherent in cross‐validation process. Abbreviations: RF, random forest; SVM, support vector machine; SP‐ECog, study partner–assessed Everyday Cognition score; CBB, Cogstate Brief Battery score; AUC, area under the receiver‐operating characteristic curve
FIGURE 4
FIGURE 4
Frequency of feature selection. (A) Non‐imputed data. (B) Imputed data. Abbreviations: RF, random forest; SVM, support vector machine; SP‐ECog or SP_ECog_score, study partner–assessed Everyday Cognition score; CBB, Cogstate Brief Battery score; FamHxAD, family history of Alzheimer's disease; Self_SMC, self‐reported subjective memory concern; Self_ECog_score, self‐assessed Everyday Cognition metrics; GDS_Score, Geriatric Depression Scale (short form) score; Det_BS, Cogstate Detection test; IDN_BS, Cogstate Identification test; OCL_BS, Cogstate One Card Learning test; ONB_BS, Cogstate One Back test
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