title: A primary estimation of the cardiometabolic risk by using artificial neural networks.
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%.
authors.affiliation: University of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia. kupusinac@gmail.com
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Kupusinac
authors.forename: Rade
authors.initials: R
authors.lastname: Doroslovački
authors.forename: Dušan
authors.initials: D
authors.lastname: Malbaški
authors.forename: Biljana
authors.initials: B
authors.lastname: Srdić
authors.forename: Edith
authors.initials: E
authors.lastname: Stokić
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D050197:Atherosclerosis / Q000097:blood / Q000175:diagnosis* / Q000473:pathology / Q000503:physiopathology; D001794:Blood Pressure; D015992:Body Mass Index; D005260:Female; D005340:Fibrinogen / Q000378:metabolism; D006801:Humans; D008055:Lipids / Q000097:blood; D008297:Male; D008659:Metabolic Diseases; D008875:Middle Aged; D016571:Neural Networks, Computer; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012737:Sex Factors; D014527:Uric Acid / Q000097:blood; D049629:Waist-Hip Ratio
chemical_list: D008055:Lipids; D014527:Uric Acid; D005340:Fibrinogen