A primary estimation of the cardiometabolic risk by using artificial neural networks
| Autor: | Biljana Srdić, Aleksandar Kupusinac, Dusan Malbaski, Rade Doroslovacki, Edith Stokić |
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| Rok vydání: | 2012 |
| Předmět: |
Adult
Male medicine.medical_specialty Diagnostic methods Adolescent Health Informatics Blood Pressure Sensitivity and Specificity Body Mass Index Sex Factors Metabolic Diseases Risk Factors Total cholesterol Internal medicine Medicine Humans cardiovascular diseases Aged Cardiometabolic risk Estimation Artificial neural network business.industry Waist-Hip Ratio Age Factors Fibrinogen Middle Aged musculoskeletal system Atherosclerosis Lipids Computer Science Applications Surgery Uric Acid cardiovascular system Cardiology Female Neural Networks Computer business Body mass index circulatory and respiratory physiology |
| Zdroj: | Computers in biology and medicine. 43(6) |
| ISSN: | 1879-0534 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
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