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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Dependence between cognitive impairment and metabolic syndrome applied to a Brazilian elderly dataset
Tadeu Junior Gross1, Renata Bezerra Araújo2, Francisco Assis Carvalho Vale3
1Department of Electrical and Computer Engineering, University of São Paulo, São Carlos, Brazil.
Abstract:
Globally, the proportion of elderly individuals in the population has increased substantially in the last few decades. However, the risk factors that should be managed in advance to ensure a natural process of mental decline due to aging remain unknown. In this study, a dataset consisting of a Brazilian elderly sample was modelled using a Bayesian Network (BN) approach to uncover connections between cognitive performance measures and potential influence factors. Regarding its structure (a Directed Acyclic Graph), it was investigated the probabilistic dependence mechanism between two variables of medical interest: the suspected risk factor known as Metabolic Syndrome (MetS) and the indicator of mental decline referred to as Cognitive Impairment (CI). In this investigation, the concept known in the context of a BN as D-separation has been employed. Results of the conducted study revealed that the dependence between MetS and Cognitive Variables (CI and its direct determinants) in fact exists and depends on both Body Mass Index (BMI) and age.
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