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...

Descripción completa

Detalles Bibliográficos
Publicado en:	Journals of Gerontology Series B: Psychological Sciences & Social Sciences Vol. 80; no. 7; pp. 1 - 12
Autores principales:	Al-Hammadi, Noor, Abouelyazid, Mahmoud, Brown, David C, Lalwani, Pooja, Devos, Hannes, Carr, David B, Babulal, Ganesh M
Formato:	Artículo
Publicado:	Oxford University Press / USA Jul2025
Materias:	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)
Acceso en línea:	Ver este registro en EBSCOhost