P4‐147: Memory Complaint, Cognitive Disorder, Chronic Disease and Life Habits in Adults and Elderly of a Brazilian Urban Community

Araújo, Renata Bezerra; Dalpubel, Daniela; Almeida, Mariana Luciano de; Ribeiro, Estela B.; Cominetti, Márcia Regina; Caramelli, Paulo; Barbosa, Maira Tonidandel; Vale, Francisco Assis Carvalho

doi:10.1016/j.jalz.2016.06.2238

Cited by 2 publications

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“…Some studies (LIU et al, 2015;DEARBORN et al, 2014;FENG et al, 2013;DIK et al, 2007) have suggested that MetS is linked to cognitive decline. Others (ARAÚJO et al, 2017;TOURNOY et al, 2010;KOMULAINEN et al, 2007) found no relationship and, some (LIU et al, 2013;LAUDISIO et al, 2008;BERG et al, 2007) even found a relative improvement in cognition. According to what has been suggested in (CHANG;LUNG;YEN, 2015;EXALTO et al, 2015;WATTS et al, 2013;SIERVO et al, 2014), the controversial findings obtained from similar data collected in different countries indicate that many possibly complex interactions between MetS and cognitive impairment may be present.…”

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confidence: 96%

“…• the investigation in (ARAÚJO et al, 2017) probably did not find a dependence between the two focus variables because the assessment was performed in the dataset as a whole, i.e. without any segregation in terms of 'Age' and BMI (or 'Weight') as suggested by the MetS-CI path in Figure 6;…”

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confidence: 99%

“…Many researchers (ARAÚJO et al, 2016;CARRILLO et al, 2009;LIVINGSTON et al, 2017) are concerned with finding risk factors for cognitive impairment (CI) -an important predictor of dementia (such as Alzheimer's disease) (ALBERT et al, 2011;NG et al, 2016). Among others, metabolic syndrome (MetS) (ALBERTI; ZIMMET; SHAW, 2005) has been increasingly recognized as a major risk factor for CI in elderly individuals (CHANG; LUNG; YEN, 2015).…”

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confidence: 99%

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Structure learning of Bayesian networks via data perturbation

Gross¹

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Structure learning of Bayesian Networks (BNs) is an NP-hard problem, and the use of sub-optimal strategies is essential in domains involving many variables. One of them is to generate multiple approximate structures and then to reduce the ensemble to a representative structure. It is possible to use the occurrence frequency (on the structures ensemble) as the criteria for accepting a dominant directed edge between two nodes and thus obtaining the single structure. In this doctoral research, it was made an analogy with an adapted one-dimensional random-walk for analytically deducing an appropriate decision threshold to such occurrence frequency. The obtained closed-form expression has been validated across benchmark datasets applying the Matthews Correlation Coefficient as the performance metric. In the experiments using a recent medical dataset, the BN resulting from the analytical cutoff-frequency captured the expected associations among nodes and also achieved better prediction performance than the BNs learned with neighbours thresholds to the computed. In literature, the feature accounted along of the perturbed structures has been the edges and not the directed edges (arcs) as in this thesis. That modified strategy still was applied to an elderly dataset to identify potential relationships between variables of medical interest but using an increased threshold instead of the predict by the proposed formula-such prudence is due to the possible social implications of the finding. The motivation behind such an application is that in spite of the proportion of elderly individuals in the population has increased substantially in the last few decades, the risk factors that should be managed in advance to ensure a natural process of mental decline due to ageing remain unknown. In the learned structural model, it was graphically investigated the probabilistic dependence mechanism between two variables of medical interest: the suspected risk factor known as Metabolic Syndrome and the indicator of mental decline referred to as Cognitive Impairment. In this investigation, the concept known in the context of BNs as D-separation has been employed. Results of the carried out study revealed that the dependence between Metabolic Syndrome and Cognitive Variables indeed exists and depends on both Body Mass Index and age.

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