期刊
ARTIFICIAL INTELLIGENCE IN MEDICINE
卷 90, 期 -, 页码 53-60出版社
ELSEVIER
DOI: 10.1016/j.artmed.2018.07.003
关键词
Population aging; Cognitive impairment; Risk factors; Metabolic syndrome; Associations discovery; Bayesian network; Directed Acyclic Graph; D-separation
资金
- Mato Grosso State Government in Brazil and its Scientific Police (POLITEC-MT)
- CAPES Brazilian Coordination for the Improvement of Higher Education Personnel
- FAPESP-Sao Paulo State Research Support Foundation [2014/50851-0]
- CNPq-National Council for Scientific and Technological Development [465755/2014-3]
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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