Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8947360 | Artificial Intelligence in Medicine | 2018 | 8 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Tadeu Junior Gross, Renata Bezerra Araújo, Francisco Assis Carvalho Vale, Michel Bessani, Carlos Dias Maciel,