Paper Info
Title
Multifactor dimensionality reduction for the analysis of obesity in a nutrigenetics context
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.
Year
DOI
Venue
2012
10.1007/978-3-642-30448-4_29
SETN
Keywords
Field
DocType
multifactor dimensionality reduction,classification mechanism,63-dimensional vector,large scale nutrigenetics study,certain accuracy,corresponding prediction model,pmdr selected factor,body mass index,pmdr method,selected factor,nutrigenetics context
Data mining,Nutrigenetics,Trait,Computer science,Multifactor dimensionality reduction,Overweight,Body mass index,Obesity,Artificial intelligence,Predictive modelling,Machine learning
Conference
Citations 
PageRank 
References 
1
0.40
3
Authors
4
Name
Order
Citations
PageRank
Katerina Karayianni110.40
Ioannis Valavanis29411.72
Keith A. Grimaldi3131.67
Konstantina Nikita451.13