Analysis of Risk Factors in Dementia Through Machine Learning.
Background: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer's disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold: on the one hand, non-modifiable factors and on the other, modifiable.Objective:...
| Publicado en: | Journal of Alzheimer's Disease Vol. 79; no. 2; pp. 845 - 862 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Sage Publications Inc.
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148281396&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148281396 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13872877 FLR jtl: Journal of Alzheimer's Disease issn: 13872877 maglogo: N pubinfo: dt: 2021 vid: 79 iid: 2 pid: 20732 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 148281396 148281396 NLM33361594 148281396 10.3233/JAD-200955 NLM33361594 148281396 ppf: 845 ppct: 17 formats: tig: atl: Analysis of Risk Factors in Dementia Through Machine Learning. aug: au: Balea-Fernandez, Francisco Javier Martinez-Vega, Beatriz Ortega, Samuel Fabelo, Himar Leon, Raquel Callico, Gustavo M. Bibao-Sieyro, Cristina affil: Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain sug: subj: Alzheimer's Disease Etiology Alzheimer's Disease Diagnosis Aged Cognition Risk Factors Tobacco Adverse Effects Aged, 80 and Over Exercise Male Sensitivity and Specificity Female Hypertension Complications Algorithms Depression Complications Human Case Control Studies Socioeconomic Factors Diabetes Mellitus, Type 2 Complications Comparative Studies Multicenter Studies Evaluation Research Validation Studies Aged: 65+ years Aged, 80 & over Male Female ab: Background: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer's disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold: on the one hand, non-modifiable factors and on the other, modifiable.Objective: This study aims to develop a processing framework based on machine learning (ML) and optimization algorithms to study sociodemographic, clinical, and analytical variables, selecting the best combination among them for an accurate discrimination between controls and subjects with major neurocognitive disorder (MNCD).Methods: This research is based on an observational-analytical design. Two research groups were established: MNCD group (n = 46) and control group (n = 38). ML and optimization algorithms were employed to automatically diagnose MNCD.Results: Twelve out of 37 variables were identified in the validation set as the most relevant for MNCD diagnosis. Sensitivity of 100%and specificity of 71%were achieved using a Random Forest classifier.Conclusion: ML is a potential tool for automatic prediction of MNCD which can be applied to relatively small preclinical and clinical data sets. These results can be interpreted to support the influence of the environment on the development of AD. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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