Analysis of postprandial lipemia as a Cardiovascular Disease risk factor using genetic and clinical information: An Artificial Neural Network perspective

Valavanis, IK; Mougiakakou, SG; Grimaldi, KA; Nikita, KS

dc.contributor.author	Valavanis, IK	en
dc.contributor.author	Mougiakakou, SG	en
dc.contributor.author	Grimaldi, KA	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T02:45:07Z
dc.date.available	2014-03-01T02:45:07Z
dc.date.issued	2008	en
dc.identifier.issn	1557170X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32159
dc.subject	Artificial Neural Network	en
dc.subject	Cardiovascular Disease	en
dc.subject	Cardiovascular Disease Risk Factor	en
dc.subject	Clinical Study	en
dc.subject	Design and Development	en
dc.subject	Genetic Variation	en
dc.subject	Genetics	en
dc.subject	Postprandial Lipemia	en
dc.subject	Risk Factors	en
dc.subject.other	Backpropagation	en
dc.subject.other	Biochemistry	en
dc.subject.other	Blood	en
dc.subject.other	Chemical vapor deposition	en
dc.subject.other	Lipids	en
dc.subject.other	Risk analysis	en
dc.subject.other	Sulfur compounds	en
dc.subject.other	Artificial neural networks	en
dc.subject.other	Cardio-vascular disease risk factors	en
dc.subject.other	Cardiovascular disease	en
dc.subject.other	Clinical informations	en
dc.subject.other	Clinical measurements	en
dc.subject.other	Clinical studies	en
dc.subject.other	Data sets	en
dc.subject.other	Design and Development	en
dc.subject.other	Environmental interactions	en
dc.subject.other	Genetic variations	en
dc.subject.other	Input variables	en
dc.subject.other	Risk factors	en
dc.subject.other	Neural networks	en
dc.title	Analysis of postprandial lipemia as a Cardiovascular Disease risk factor using genetic and clinical information: An Artificial Neural Network perspective	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IEMBS.2008.4650240	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IEMBS.2008.4650240	en
heal.identifier.secondary	4650240	en
heal.publicationDate	2008	en
heal.abstract	Clinical studies indicate that exaggerated postprandial lipemia is linked to the progression of atherosclerosis, leading cause of Cardiovascular Diseases (CVD). CVD is a multi-factorial disease with complex etiology and according to the literature postprandial Triglycerides (TG) can be used as an independent CVD risk factor. Aim of the current study is to construct an Artificial Neural Network (ANN) based system for the identification of the most important gene-gene and/or gene-environmental interactions that contribute to a fast or slow postprandial metabolism of TG in blood and consequently to investigate the causality of postprandial TG response. The design and development of the system is based on a dataset of 213 subjects who underwent a two meals fatty prandial protocol. For each of the subjects a total of 30 input variables corresponding to genetic variations, sex, age and fasting levels of clinical measurements were known. Those variables provide input to the system, which is based on the combined use of Parameter Decreasing Method (PDM) and an ANN. The system was able to identify the ten (10) most informative variables and achieve a mean accuracy equal to 85.21%. © 2008 IEEE.	en
heal.journalName	Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - ""Personalized Healthcare through Technology""	en
dc.identifier.doi	10.1109/IEMBS.2008.4650240	en
dc.identifier.volume	2008	en
dc.identifier.spage	4609	en
dc.identifier.epage	4612	en