%0 Journal Article
%T A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context
%A Ioannis K Valavanis
%A Stavroula G Mougiakakou
%A Keith A Grimaldi
%A Konstantina S Nikita
%J BMC Bioinformatics
%D 2010
%I BioMed Central
%R 10.1186/1471-2105-11-453
%X PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets.The ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics.Cardiovascular disease (CVD) is a family of common multifactorial diseases, e.g. coronary heart disease (CHD), cerebrovascular disease, hypertension, and heart failure, which develop as a consequence of interactions between the "initial" conditions, coded in a person's genotype, and exposure to environmental factors (e.g. nutrition, smoking) [1]. Latest statistics shows that CVDs are the leading cause of death and morbidity worldwide and according to the World Health Organization (WHO) an estimated 16.7 million - or 29.2% of deaths - result from the various forms of CVD. However, many CVDs are preventable by action on the primary environmental risk factors such as unhealthy diet, physical inactivity, and smoking [2]. Obesity comprises one of the most important independent CVD risk factors and many large scale studies have shown a po
%U http://www.biomedcentral.com/1471-2105/11/453