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

Valavanis, Ioannis K; Mougiakakou, Stavroula G; Grimaldi, Keith A; Nikita, Konstantina S (2008). Analysis of postprandial lipemia as a Cardiovascular Disease risk factor using genetic and clinical information: an Artificial Neural Network perspective. IEEE Engineering in Medicine and Biology Society conference proceedings, 2008, pp. 4609-4612. Piscataway, N.J.: IEEE Service Center 10.1109/IEMBS.2008.4650240

Full text not available from this repository.

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%.

Item Type:	Journal Article (Original Article)
Division/Institute:	04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition
UniBE Contributor:	Mougiakakou, Stavroula
ISSN:	1557-170X
Publisher:	IEEE Service Center
Language:	English
Submitter:	Factscience Import
Date Deposited:	04 Oct 2013 15:23
Last Modified:	05 Dec 2022 14:25
Publisher DOI:	10.1109/IEMBS.2008.4650240
PubMed ID:	19163743
Web of Science ID:	000262404502349
URI:	https://boris.unibe.ch/id/eprint/37253 (FactScience: 207277)