Assessing the Utility of a Knowledge Based Algorithm for Classifying Alzheimer disease and Mild Cognitive Impairment in the Recruitment and Retention for Alzheimer’s Disease Diversity Cohorts in the ADSP (READD‐ADSP) Project

Our findings show that culturally sensitive adjusted cut-offs and inclusion of domain average cut-offs improved the accuracy of decision tree model classifications. These findings suggest that implementation of decision tree model approaches in the READD-ADSP dataset can accurately and efficiently classify individuals. Finally, we plan improve overall accuracy by using a hybrid method where models classify individuals who are clearly Unaffected or AD while more complex cases will be assigned to human adjudication.

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Publication Details

Journal
Alzheimer s & Dementia
Published
2024-12-01
DOI
https://doi.org/10.1002/alz.091433
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
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article

Assessing the Utility of a Knowledge Based Algorithm for Classifying Alzheimer disease and Mild Cognitive Impairment in the Recruitment and Retention for Alzheimer’s Disease Diversity Cohorts in the ADSP (READD‐ADSP) Project

Andrew Zaman, Tina Duke, Giuseppe Tosto, Jonathan L. Haines et al.
Alzheimer s & Dementia
Dementia and Cognitive Impairment Research
article

Assessing the Utility of a Knowledge Based Algorithm for Classifying Alzheimer disease and Mild Cognitive Impairment in the Recruitment and Retention for Alzheimer’s Disease Diversity Cohorts in the ADSP (READD‐ADSP) Project

Andrew Zaman, Tina Duke, Giuseppe Tosto, Jonathan L. Haines, Farid Rajabli, Goldie S. Byrd, Michael L. Cuccaro, Jeffery M. Vance, Kara L. Hamilton‐Nelson, Katrina Celis, Christiane Reitz, Takiyah D. Starks, Reneé A. Laux, Paula Ogrocki, Pedro Mena, Jóse Javier Sánchez, Allison Caban‐Holt, Larry D. Adams, Juan Manuel Muñoz Céspedes, Jenny Chavez, Katalina McInerney, Margaret A Pericak‐Vance
article en

Abstract

Our findings show that culturally sensitive adjusted cut-offs and inclusion of domain average cut-offs improved the accuracy of decision tree model classifications. These findings suggest that implementation of decision tree model approaches in the READD-ADSP dataset can accurately and efficiently classify individuals. Finally, we plan improve overall accuracy by using a hybrid method where models classify individuals who are clearly Unaffected or AD while more complex cases will be assigned to human adjudication.

Alzheimer s & DementiaVol. 20(S3)
University of Miami (US), University Hospitals of Cleveland (US), Dr. John T. Macdonald Foundation (US), University School (US), Wake Forest University (US), Case Western Reserve University (US), Columbia University (US)
Reduced inequalities
Openalex Percentile: Top 66%
Dementia and Cognitive Impairment Research
DOIOpen Access PDFOpenAlex
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