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

<p>Abstract</p> <p>Background</p> <p>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 by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

Behavioral Economics and the Public Sector

This thesis consists of four essays dealing with topics that are relevant for the public sector. The essays cover diverse issues of economics partly overlapping with political science. The topics reach from the taxation of labor over monetary policy to preferences over voting institutions. Throughout this thesis it is, in contrast to classical economics, not assumed that humans are necessarily fully rational. Once full rationality is no longer assumed, experiments become an important tool to learn about human behavior. Consequently, most of the work in this thesis makes use of economic experiments

Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies

Agent-based computational economics, Evolutionary algorithm, Genetic algorithm, Binary encoding, Premature convergence, C63, C73, D43, D83,

On the Fractal Characterization of a System for Tradings on Eurozone Stocks

It is a common habit among practitioners to maintain under strong control the behavior of a bunch of indexes that are known to capture the movements of Eurozone stocks. Baltic Dry Index (BDI), RJ/CRB Commodity Price Index (CRB), Chicago Board Options Exchange Volatility Index (VIX) and Deutsche Bank G10 Currency Future Harvest Index (DBHI), in fact, are supposed to exhibit a kind of anticipatory behavior with respect to that of Eurozone economy: understanding their dynamics should therefore imply to know in advance how the economic system will behave. The rationale of this chapter is to verify to what extent the use of tools relying on chaos theory and complexity studies (in our case: multiscaling analysis) can be of any help to capture such anticipatory movements. To do this, we performed two separate tasks: we evaluated the Hurst exponent of the aforementioned indexes using a set of techniques, to give robustness to the results; we then moved to compute for each of them the H\uf6lderian function values. The results suggested us the track along which developing a trading system based on the fractal characterization of the Eurostoxx 50 index whose performance will be provided and discussed as well