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...
| Publicado en: | Journals of Gerontology Series B: Psychological Sciences & Social Sciences Vol. 80; no. 7; pp. 1 - 12 |
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| Autores principales: | , , , , , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Jul2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=189082023&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 189082023 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10795014 JGB jtl: Journals of Gerontology Series B: Psychological Sciences & Social Sciences issn: 10795014 maglogo: N pubinfo: dt: Jul2025 vid: 80 iid: 7 pid: 622 pub: Oxford University Press / USA artinfo: ui: 189082023 10.1093/geronb/gbaf023 ppf: 1 ppct: 11 formats: tig: atl: Integrating Machine Learning and Environmental and Genetic Risk Factors for the Early Detection of Preclinical Alzheimer's Disease. aug: au: Al-Hammadi, Noor Abouelyazid, Mahmoud Brown, David C Lalwani, Pooja Devos, Hannes Carr, David B Babulal, Ganesh M affil: Department of Neurology, Washington University in St. Louis, Missouri, USA Electrical and Computer Engineering Department, Purdue University, West Lafayette, Indiana, USA Department of Biology, Duke University, Durham, North Carolina, USA Department of Physical Therapy, Rehabilitation Science, and Athletic Training, University of Kansas Medical Center, Kansas City, Kansas, USA University of Kansas Alzheimer's Disease Research Center, University of Kansas Medical Center, Kansas City, Kansas, USA Department of Medicine, Washington University in St. Louis, Missouri, USA Institute of Public Health, Washington University in St. Louis, St. Louis, Missouri, USA Department of Psychology, Faculty of Humanities, University of Johannesburg, South Africa su: 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) sug: subj: Health services accessibility Socioeconomic factors Health equity Public health Social isolation Health and Welfare Funds Diagnostic Imaging Centers 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) keyword: Area deprivation index Naturalistic driving behavior Predictive modeling Resampling/bootstrapping methods Area deprivation index Naturalistic driving behavior Predictive modeling Resampling/bootstrapping methods ab: 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 examined model performance with two bootstrapping procedures. Methods Data were drawn from a longitudinal cohort that required participants to be age 65 or older, cognitively normal at baseline, and active drivers, defined as taking at least one trip a week. Naturalistic driving data were collected using a commercial datalogger. Biomarker positivity was determined via amyloid pathology using cerebrospinal fluid and positron emission tomography imaging. ADI was captured based on geocoding latitude and longitude to derive a national ranking for the specific location (home or unique destination). Machine learning algorithms classified preclinical AD. Each individual model's predictive ability was confirmed in a 20% testing dataset with 100 rounds of resampling with and without replacement. Results Among 292 participants (n = 2,792 observations), including ADI of trip destinations, participants' home ADI, and frequency of trips to the same ADI led to a slight but notable improvement in predicting preclinical AD. The ensemble model demonstrated superior predictive performance, highlighting the potential of integrating multiple models for early AD detection. Discussion Our findings underscore the importance of incorporating socioeconomic and environmental variables, such as neighborhood deprivation, in predicting preclinical AD. Addressing socioeconomic disparities through public health strategies is crucial for mitigating AD risk and enhancing the quality of life for older adults. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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