A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context

Background: Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Ioannis K. Valavanis, Stavroula G. Mougiakakou, Keith A. Grimaldi, Konstantina S. Nikita
• Keywords: BMCBI 2010, Genetic Variations, Independent Risk Factor, Multi-layer Feed-forward Ann