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Integrating Machine Learning and Environmental and Genetic Risk Factors for the Early Detection of Preclinical Alzheimer's Disease.

Objective This study classified preclinical Alzheimer's disease (AD) using cognitive screening, neighborhood deprivation via the area deprivation index (ADI), and sociodemographic and genetic risk factors. Additionally, it compared the predictive accuracy of multiple machine learning algorithms and...

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Published in:Journals of Gerontology Series B: Psychological Sciences & Social Sciences Vol. 80; no. 7; pp. 1 - 12
Main Authors: Al-Hammadi, Noor, Abouelyazid, Mahmoud, Brown, David C, Lalwani, Pooja, Devos, Hannes, Carr, David B, Babulal, Ganesh M
Format: Article
Published: Oxford University Press / USA Jul2025
Subjects:
Health services accessibility
Socioeconomic factors
Health equity
Public health
Social isolation
Alzheimer's disease risk factors
Alzheimer's disease diagnosis
Genetics of Alzheimer's disease
Predictive tests
Random forest algorithms
Receiver operating characteristic curves
T-test (Statistics)
Statistical significance
Research funding
Research evaluation
Positron emission tomography
Descriptive statistics
Longitudinal method
Support vector machines
Neuropsychological tests
Geographic information systems
Cognition disorders
Environmental exposure
Early diagnosis
Machine learning
Disease susceptibility
Data analysis software
Neighborhood characteristics
Biomarkers
Cerebrospinal fluid
Sensitivity & specificity (Statistics)
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