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Title: Analysis of Risk Factors in Dementia Through Machine Learning
Authors: Balea Fernández, Francisco Javier 
Martínez Vega, Beatriz 
Ortega Sarmiento, Samuel 
Fabelo Gómez, Himar Antonio 
León Martín, Sonia Raquel 
Marrero Callicó, Gustavo Iván 
Bilbao Sieyro, Cristina 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Alzheimer’s disease
machine learning
neurocognitive disorders
risk factors
Issue Date: 2021
Project: Identificación Hiperespectral de Tumores Cerebrales (Ithaca) 
Plataforma H2/Sw Distribuida Para El Procesamiento Inteligente de Información Sensorial Heterogenea en Aplicaciones de Supervisión de Grandes Espacios Naturales 
Journal: Journal of Alzheimer's Disease 
Abstract: 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.
ISSN: 1387-2877
DOI: 10.3233/JAD-200955
Source: Journal of Alzheimer's Disease[ISSN 1387-2877],v. 79 (2), p. 845-861
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