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Comput Biol Med. 2013 Jul;43(6):751-7. doi: 10.1016/j.compbiomed.2013.04.001. Epub 2013 Apr 6.

A primary estimation of the cardiometabolic risk by using artificial neural networks.

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  • 1University of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia. kupusinac@gmail.com

Abstract

Estimation of the cardiometabolic risk (CMR) has a leading role in the early prevention of atherosclerosis and cardiovascular diseases. The CMR estimation can be separated into two parts: primary estimation (PE-CMR) that includes easily-obtained, non-invasive and low-cost diagnostic methods and secondary estimation (SE-CMR) involving complex, invasive and/or expensive diagnostic methods. This paper presents a PE-CMR solution based on artificial neural networks (ANN) as it would be of great interest to develop a procedure for PE-CMR that would save time and money by extracting the persons with potentially higher CMR and conducting complete SE-CMR tests only on them. ANN inputs are values obtained by using PE-CMR methods, i.e. primary risk factors: gender, age, waist-to-height ratio, body mass index, systolic and diastolic blood pressures. ANN output is cmr-coefficient obtained from the number of disturbances in biochemical indicators, i.e. secondary risk factors: HDL-, LDL- and total cholesterol, triglycerides, glycemia, fibrinogen and uric acid. ANN training and testing are done by dataset that includes 1281 persons. The accuracy of our solution is 82.76%.

Copyright © 2013 Elsevier Ltd. All rights reserved.

[PubMed - indexed for MEDLINE]
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