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Multifactor Dimensionality Reduction for the Analysis of Obesity in a Nutrigenetics Context
Katerina Karayianni1, Ioannis Valavanis2, Keith Grimaldi3, and Konstantina Nikita1
1School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str. Zografos, 15780, Athens, Greece
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2Institute of Biological Research and Biotechnology, National Hellenic Research Foundation, Vas. Konstantinou 46, 11635, Athens, Greece
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3Biomedical Engineering Laboratory, Institute of Communication and Computer Systems, National Technical University of Athens, Polytechniou Str. Zografos, 15780, Athens, Greece
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Abstract. The current work aims to study within a nutrigenetics context the multifactorial trait beneath obesity. To this end, the use of parallel Multifactor Dimensionality Reduction (pMDR) is investigated towards the identification of i) factors that have an impact to obesity onset solely or interacting with each other and ii) rules that describe the interactions among them. Data have been obtained from a large scale nutrigenetics study and each subject, characterized as normal or overweight based on Body Mass Index (BMI), is featured a 63-dimensional vector describing his/her genetic variations and nutritional habits. pMDR method was used to reduce the initial set of factors into subsets that can classify a subject into either normal or overweight with a certain accuracy and are further used by corresponding prediction models. Results showed that pMDR selected factors associated to obesity and constructed predictive models showing a good generalization ability. Rules describing interactions of the selected factors were extracted, thus enlightening the classification mechanism of the constructed model.
Keywords: nutrigenetics, obesity, Multifactor Dimensionality Reduction, prediction model
LNAI 7297, p. 231 ff.
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© Springer-Verlag Berlin Heidelberg 2012