Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Journal of Medical Systems
Published in: Journal of Medical Systems 6/2016

01-06-2016 | Transactional Processing Systems

Hybrid EANN-EA System for the Primary Estimation of Cardiometabolic Risk

Authors: Aleksandar Kupusinac, Edita Stokic, Ilija Kovacevic

Published in: Journal of Medical Systems | Issue 6/2016

Login to get access

Abstract

The most important part of the early prevention of atherosclerosis and cardiovascular diseases is the estimation of the cardiometabolic risk (CMR). The CMR estimation can be divided into two phases. The first phase is called primary estimation of CMR (PE-CMR) and includes solely diagnostic methods that are non-invasive, easily-obtained, and low-cost. Since cardiovascular diseases are among the main causes of death in the world, it would be significant for regional health strategies to develop an intelligent software system for PE-CMR that would save time and money by extracting the persons with potentially higher CMR and conducting complete tests only on them. The development of such a software system has few limitations - dataset can be very large, data can not be collected at the same time and the same place (eg. data can be collected at different health institutions) and data of some other region are not applicable since every population has own features. This paper presents a MATLAB solution for PE-CMR based on the ensemble of well-learned artificial neural networks guided by evolutionary algorithm or shortly EANN-EA system. Our solution is suitable for research of CMR in population of some region and its accuracy is above 90 %.
Literature
1.
Rutter, M. K, Meigs, J. B, Sullivan, L. M, D’Agostino, R. B. Sr, Wilson, P. W, Insulin resistance, the metabolic syndrome, and incident cardiovascular events in the Framingham Offspring Study. Diabetes 54:3252–3257, 2005.CrossRefPubMed
2.
Appel, S. J, Jones, E. D, Kennedy-Malone, L., Central obesity and the metabolic syndrome: implications for primary care providers. J. Am. Acad. Nurse Pract. 16:335–342, 2004.CrossRefPubMed
3.
Stokic, E., Tomic-Naglic, D., Deric, M., Jorga, J., Therapeutic options for treatment of cardiometabolic risk. Med. Pregl. 62 Suppl. 3:54–58, 2009.
4.
Stokic, E., Brtka, V., Srdic, B., The synthesis of the rough set model for the better applicability of sagittal abdominal diameter in identifying high risk patients. Comput. Biol. Med. 2010(40):786–790, 2010.CrossRef
5.
Berg, A. H, and Scherer, P. E, Adipose tissue, inflammation, and cardiovascular disease. Circ. Res. 96:939–949, 2005.CrossRefPubMed
6.
Misra, A., and Vikram, N. K, Clinical and patophysiological consequences of abdominal adiposity and abdominal adipose tissue depots. Nutrition 19:457–466, 2003.CrossRefPubMed
7.
Recio-Rodriguez, J. I, Gomez-Marcos, M. A, Patino-Alonso, M. C, Agudo-Conde, C., Rodriguez-Sanchez, E., Garcia-Ortiz, L., Abdominal obesity vs general obesity for identifying arterial stiffness, subclinical atherosclerosis and wave reflection in healthy, diabetics and hypertensive. BMC Cardiovasc. Disord. 12: 3, 2012. doi:10.​1186/​1471-2261-12-3.CrossRefPubMedPubMedCentral
8.
Rosolova, H., and Nussbaumerova, B., Cardio-metabolic risk prediction should be superior to cardiovascular risk assessment in primary prevention of cardiovascular diseases. EPMA J. 2:15–26, 2011.CrossRefPubMedPubMedCentral
9.
Kupusinac, A., Doroslovacki, R., Malbaški, D., Srdic, B., Stokic, E., A primary estimation of the cardiometabolic risk by using artificial neural networks. Comput. Biol. Med. 2013(43):751–757, 2013.CrossRef
10.
Roeters van Lennep, J. E, Westerveld, H. T, Erkelens, D. W, van der Wall, E. E, Risk factors for coronary heart disease: implications of gender. Cardiovasc. Res. 53:538–549, 2002.CrossRefPubMed
11.
Mercuro, G., Deidda, M., Piras, A., Dessalvi, C. C, Maffei, S., Rosano, G. M, Gender determinants of cardiovascular risk factors and diseases. J. Cardiovasc. Med. (Hagerstown) 11:207–220, 2010.CrossRef
12.
World Health Organization, Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech. Rep. Ser. 894:1–253, 2000.
13.
14.
Wells, J. C, and Victora, C. G, Indices of whole-body and central adiposity for evaluating the metabolic load of obesity. Int J Obes 29:483–489, 2005.CrossRef
15.
van Dis, I., Kromhout, D., Geleijnse, J. M, Boer, J. M, Verschuren, W. M, Body mass index and waist circumference predict both 10-year nonfatal and fatal cardiovascular disease risk: study conducted in 20,000 Dutch men and women aged 20-65 years. Eur. J. Cardiovasc. Prev. Rehabil. 16:729–734, 2009.CrossRefPubMed
16.
de Koning, L., Merchant, A. T, Pogue, J., Anand, S. S, Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur. Heart J. 28:850–856, 2007.CrossRefPubMed
17.
Browning, L. M, Hsieh, S. D, Ashwell, M., A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutr. Res. Rev. 23:247–269, 2010.CrossRefPubMed
18.
Cristo Rodriguez Perez, M. D, Cabrera De Leon, A., Aguirre-Jaime, A., Dominguez Coello, S., Brito Diaz, B., Almeida Gonzalez, D., Borges Alamo, C., Castillo Rodriguez, J. C, Carrillo Fernandez, L., Gonzalez Hernandez, A., Aleman Sanchez, J. J, The waist to height ratio as an index of cardiovascular risk and diabetes. Med. Clin. (Barc) 3;134:386–391, 2010.CrossRef
19.
Ashwell, M., Gunn, P., Gibson, S., Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes. Rev. 13:275–286, 2012.CrossRefPubMed
20.
Honjo, K., Iso, H., Inoue, M., Tsugane, S., Adult height and the risk of cardiovascular disease among middle aged men and women in Japan. Eur. J. Epidemiol. 26:13–21, 2011.CrossRefPubMed
21.
Kupusinac, A., Stokic, E., Srdic, B., Determination of WHtR Limit for Predicting Hyperglycemia in Obese Persons by Using Artificial Neural Networks. TEM J. 1:270–272, 2012.
22.
Bose, S., Krishnamoorthy, P., Varanasi, A., Nair, J., Schutta, M., Braunstein, S., Iqbal, N., Schwartz, S., St Clair, C., Master, S. R., Rader, D. J, Reilly, M. P, Mehta, N. N, Measurement of waist circumference predicts coronary atherosclerosis beyond plasma adipokines. Obesity (Silver Spring) 21:E118–E123, 2013.CrossRef
23.
Trials of Hypertension prevention Collaborative Research Group, The effects of nonpharmacologic interventions on blood pressure of persons with high-normal levels: results of the trials of hypertension prevention, phase I. JAMA 267:1213–1220, 1992.CrossRef
24.
Cifkova, R., Erdine, S., Fagard, R., Farsang, C., Heagerty, A. M, Kiowski, W., Kjeldsen, S., Luscher, T., Mallion, J. M., Mancia, G., Poulter, N., Rahn, K. H, Rodicio, J. L, Ruilope, L. M, van Zwieten, P., Waeber, B., Willims, B., Zanchetti, A., ESH/ESC Hypertension Guidelines Committee, Practice guidelines for primary care physicians: 2003 ESH/ESC hypertension guidelines. J. Hypertens. 21:1779–1786, 2003.CrossRefPubMed
25.
Assmann, G., Cullen, P., Evers, T., Petzinna, D., Schulte, H., Importance of arterial pulse pressure as a predictor of coronary heart disease risk in PROCAM. Eur. Heart. J. 26:2120–2126, 2005.CrossRefPubMed
26.
Beer, C., Alfonso, H., Flicker, L., Norman, P. E, Hankey, G. J, Almeida, O. P, Traditional risk factors for incident cardiovascular events have limited importance in later life compared with the health in men study cardiovascular risk score. Stroke 42:952–959, 2011.CrossRefPubMed
27.
Chiu, C. H, Application of Back-propagation Neural Network to Categorization of Physical Fitness Levels of Taiwanese Females. J. Med. Biol. Eng. 31:31–35, 2011.CrossRef
28.
Amato, F., Lopez, A., Pena-Mendez, E. M, Vanhara, P., Hampl, A., Havel, J., Artificial neural networks in medical diagnosis. J. Appl. Biomed. 11:47–58, 2013.CrossRef
29.
Patil, S. B, and Kumaraswamy, Y. S, Intelligent and Effective Heart Attack Prediction System Using Data Mining and Artificial Neural Network. Eur. J. Sci. Res. 31:642–656, 2009.
30.
Atkov, O., Gorokhova, S., Sboev, A., Generozov, E., Muraseyeva, E., Moroshkina, S., Cherniy, N., Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters. J. Cardiol. 59:190–194, 2012.CrossRefPubMed
31.
Karabulut, E., and Ibrikci, T., Effective diagnosis of coronary artery disease using the rotation forest ensemble method. J. Med. Syst. 36:3011–3018, 2012.CrossRefPubMed
32.
Heydari, S. T, Ayatollahi, S. M. T, Zare, N., Comparison of Artificial Neural Networks with Logistic Regression for Detection of Obesity. J. Med. Sys. 36:2449–2454, 2012.CrossRef
33.
Lin, C. C, Bai, Y. M, Chen, J. Y, Hwang, T. J, Chen, T. T, Chiu, H. W, Li, Y. C, Easy and Low-Cost Identification of Metabolic Syndrome in Patients Treated With Second-Generation Antipsychotics: Artificial Neural Network and Logistic Regression Models. J. Clin. Psychiatry 71:225–234, 2010.CrossRefPubMed
34.
Hirose, H., Takayama, T., Hozawa, S., Hibi, T., Saito, I., Prediction of metabolic syndrome using artificial neural network system based on clinical data including insulin resistance index and serum adiponectin. Comput. Biol. Med. 41:1051–1056, 2011.CrossRefPubMed
35.
36.
Friedewald, W T, Levy, R I, Fredrickson, D S, Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin. Chem. 18:499–502, 1972.PubMed
37.
Ashlock, D.: Evolutionary Computation for Modeling and Optimization. Springer. ISBN 0-387-22196-4 (2006)
38.
Sweetnam, P. M, Thomas, H. F, Yarnell, J. W. G, Beswick, A. D, Baker, I. A, Elwood, P. C, Fibrinogen, viscosity and the 10-year incidence of ischaemic heart disease. Eur. Heart J. 17:1814–1820, 1996.CrossRefPubMed
39.
Laakso, M., Hyperglycemia and cardiovascular disease in type 2 diabetes. Diabetes 48:937–942, 1999.CrossRefPubMed
40.
Expert Panel on Detection, and Treatment of High Blood Cholesterol in Adults, Executive summary of the third report of the National. Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA 285:2486–2497, 2001.
41.
Grundy, S. M, Cleeman, J. I, Daniels, S. R, Donato, K. A, Eckel, R. H, Franklin, B. A, Gordon, D. J, Krauss, R. M, Savage, P. J, Smith, S. C, Spertus, J. A, Costa, F., Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 112:2735–2752, 2005.CrossRefPubMed
42.
Baggio, B., Budakovic, A., Perissinotto, E., Maggi, S., Cantaro, S., Enzi, G., Grigoletto, F., ILSA Working Group, Atherosclerotic risk factors and renal function in the elderly: the role of hyperfibrinogenaemia and smoking. Results from the Italian Longitudinal Study on Ageing (ILSA). Nephrol. Dial. Transplant. 20:114–123, 2005.CrossRefPubMed
43.
Shai, I., Pischon, T., Hu, F. B, Ascherio, A., Rifai, N., Rimm, E. B, Soluble intercellular adhesion molecules, soluble vascular cell adhesion molecules, and risk of coronary heart disease. Obesity (Silver Spring) 14: 2099–2106, 2006.CrossRef
44.
45.
Metadata
Title
Hybrid EANN-EA System for the Primary Estimation of Cardiometabolic Risk
Authors
Aleksandar Kupusinac
Edita Stokic
Ilija Kovacevic
Publication date
01-06-2016
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 6/2016
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-016-0498-1

Other articles of this Issue 6/2016

Journal of Medical Systems 6/2016 Go to the issue

Mobile Systems

An Analysis of WhatsApp Usage for Communication Between Consulting and Emergency Physicians

Patient Facing Systems

Telemedicine Use in Rural Native American Communities in the Era of the ACA: a Systematic Literature Review

Education & Training

Digital Environment for Movement Control in Surgical Skill Training

Transactional Processing Systems

Sensitive Patient Data Hiding using a ROI Reversible Steganography Scheme for DICOM Images

Transactional Processing Systems

A Computer-Aided Diagnosis System for Measuring Carotid Artery Intima-Media Thickness (IMT) Using Quaternion Vectors

Transactional Processing Systems

Regional Image Features Model for Automatic Classification between Normal and Glaucoma in Fundus and Scanning Laser Ophthalmoscopy (SLO) Images