Multifactor dimensionality reduction for the analysis of obesity in a nutrigenetics context

Karayianni, K; Valavanis, I; Grimaldi, K; Nikita, K

dc.contributor.author	Karayianni, K	en
dc.contributor.author	Valavanis, I	en
dc.contributor.author	Grimaldi, K	en
dc.contributor.author	Nikita, K	en
dc.date.accessioned	2014-03-01T02:53:55Z
dc.date.available	2014-03-01T02:53:55Z
dc.date.issued	2012	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36488
dc.subject	Multifactor Dimensionality Reduction	en
dc.subject	nutrigenetics	en
dc.subject	obesity	en
dc.subject	prediction model	en
dc.subject.other	Classification mechanism	en
dc.subject.other	Dimensionality reduction	en
dc.subject.other	Generalization ability	en
dc.subject.other	Genetic variation	en
dc.subject.other	Multi-factor	en
dc.subject.other	nutrigenetics	en
dc.subject.other	obesity	en
dc.subject.other	On-body	en
dc.subject.other	Prediction model	en
dc.subject.other	Predictive models	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Mathematical models	en
dc.subject.other	Nutrition	en
dc.title	Multifactor dimensionality reduction for the analysis of obesity in a nutrigenetics context	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-30448-4_29	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-30448-4_29	en
heal.publicationDate	2012	en
heal.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. © 2012 Springer-Verlag.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-30448-4_29	en
dc.identifier.volume	7297 LNCS	en
dc.identifier.spage	231	en
dc.identifier.epage	238	en