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

Computers in Biology and Medicine

Volumn 43, Issue 6, 2013, Pages 751-757

  Kupusinac, Aleksandar ^a   Doroslovački, Rade ^a   Malbaški, Dušan ^a   Srdić, Biljana ^b   Stokić, Edith ^b  

^a UNIVERSITY OF NOVI SAD   (Serbia)

^b UNIVERSITY OF NOVI SAD   (Serbia)

Author keywords

Artificial neural networks; Cardiometabolic risk; Primary estimation

Indexed keywords

ANN TRAININGS; BIOCHEMICAL INDICATORS; BODY MASS INDEX; CARDIO-VASCULAR DISEASE; DIAGNOSTIC METHODS; RISK FACTORS; SYSTOLIC AND DIASTOLIC BLOOD PRESSURES; TOTAL CHOLESTEROLS;

BLOOD PRESSURE; DISEASES; ESTIMATION; NEURAL NETWORKS; ORGANIC ACIDS;

RISK PERCEPTION;

FIBRINOGEN; HIGH DENSITY LIPOPROTEIN CHOLESTEROL; LOW DENSITY LIPOPROTEIN CHOLESTEROL; TRIACYLGLYCEROL; URIC ACID;

ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; BODY MASS; CARDIOMETABOLIC RISK; CLINICAL PRACTICE; DIASTOLIC BLOOD PRESSURE; FEMALE; GLUCOSE BLOOD LEVEL; HUMAN; MAJOR CLINICAL STUDY; MALE; PRIORITY JOURNAL; SYSTOLIC BLOOD PRESSURE; WAIST TO HEIGHT RATIO;

ADOLESCENT; ADULT; AGE FACTORS; AGED; ATHEROSCLEROSIS; BLOOD PRESSURE; BODY MASS INDEX; FEMALE; FIBRINOGEN; HUMANS; LIPIDS; MALE; METABOLIC DISEASES; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); RISK FACTORS; SENSITIVITY AND SPECIFICITY; SEX FACTORS; URIC ACID; WAIST-HIP RATIO;

EID: 84877687662     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2013.04.001     Document Type: Article

References (50)

1. Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks, WHO, Geneva, December 2009, assessed at http://www.who.int/healthinfo/global_burden_disease/GlobalHealthRisks_report_full.pdf.
2. Strategy for Prevention and Control of Chronic Non-infectious Diseases of the Republic of Serbia, available on (in Serbian). http://www.minzdravlja.info/downloads/Zakoni/Strategije/Strategija%20Za%20Prevenciju%20I%20Kontrolu%20Hronicnih%20Nezaraznih%20Bolesti.pdf.
3. Rutter M.K., Meigs J.B., Sullivan L.M., D'Agostino R.B., Wilson P.W. Insulin resistance, the metabolic syndrome, and incident cardiovascular events in the Framingham offspring study. Diabetes 2005, 54(11):3252-3257.
4. 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. 2004, 16(8):335-342.
5. Stokić E., Tomić-Naglić D., Derić M., Jorga J. Therapeutic options for treatment of cardiometabolic risk. Med. Pregl. 2009, 62(Suppl. 3):54-58.
6. Stokić E., Brtka V., Srdić 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(September 9):786-790.
7. Berg A.H., Scherer P.E. Adipose tissue, inflammation, and cardiovascular disease. Circ. Res. 2005, 96:939-949.
8. Misra A., Vikram N.K. Clinical and pathophysiological consequences of abdominal adiposity and abdominal adipose tissue depots. Nutrition 2003, 19:457-466.
9. Grujić V., Martinov-Cvejin M., Ač-Nikolić E., Nićiforović-Šurković O. Epidemiology of obesity in adult population of Vojvodina. Med. Pregl. 2005, LVIII(5-6):292-295. (in Serbian).
10. Rosolova H., Nussbaumerova B. Cardio-metabolic risk prediction should be superior to cardiovascular risk assessment in primary prevention of cardiovascular diseases. EPMA J. 2011, 2:15-26.
11. Cross S.S., Harrison R.F., Kennedy R.L. Introduction to neural networks. Lancet 1995, 346:1075-1079.
12. Anselmino M., Malmberg K., Rydèn L., Öhrvik J. A gluco-metabolic risk index with cardiovascular risk stratification potential in patients with coronary artery disease. Diabetes Vasc. Dis. Res. 2009, 6(2):62-70.
13. Baxt W.G. Application of artificial neural networks to clinical medicine. Lancet 1995, 346:1135-1138.
14. Patil S.B., Kumaraswamy Y.S. Intelligent and effective heart attack prediction system using data mining and artificial neural network. Eur. J. Sci. Res. 2009, 31(4):642-656.
15. 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. 2011, 41:1051-1056.
16. 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. Psychiat. 2010, 71(3):225-234.
17. World Health Organization, Obesity: Preventing and Managing the Global Epidemic, Report of a WHO Consultation, World Health Organization Technical Report Series 894, 2000, pp. 1-253.
18. 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 2011, 20. November 23, doi: http://dx.doi.org/10.1111/j.1467-789X.2011.00952.x.
19. Practice guidelines for primary care physicians: 2003 ESH/ESC hypertension guidelines, J. Hypertension, 21(10) (2003) 1779-1786.
20. Cifkova R, Erdine S, Fagard R, Farsang C, Heagerty AM, Kiowski W, Kjeldsen S, Lüscher T, Mallion JM, Mancia G, Poulter N, Rahn KH, Rodicio JL, Ruilope LM, van Zwieten P, Waeber B, Williams B, Zanchetti A; ESH/ESC Hypertension Guidelines Committee. 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. J. Am. Med. Assoc. 267 (1992) 1213-1220.
21. 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. 1972, 18:499-502.
22. 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), J. Am. Med. Assoc. 285 (2001) 2486-2497.
23. 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 Jr 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 2005, 112:2735-2752.
24. 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. 1996, 17:1814-1820.
25. 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 2006, 14(11):2099-2106.
26. 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. 2005, 20(1):114-123.
27. Takayama S., Kawamoto R., Kusunoki T., Abe M., Onji M. Uric acid is an independent risk factor for carotid atherosclerosis in a Japanese elderly population without metabolic syndrome. Cardiovasc. Diabetol. 2012, 11:2. 10.1186/1475-2840-11-2.
28. Feig D.I., Kang D.H., Johnson R.J. Uric acid and cardiovascular risk. N. Engl. J. Med. 2008, 359(17):1811-1821.
29. Laakso M. Hyperglycemia and cardiovascular disease in type 2 diabetes. Diabetes 1999, 48:937-942.
30. Cybenko D.L. Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst. 1989, 2:303-314.
31. D.L. Chester, Why two hidden layers are better than one, in: Proceedings of the International Joint Conference on Neural Networks, Washington, USA, 1990, pp. 265-268.
32. Masters T. Practical Neural Network Recipes in C++ 1993, Academic Press, New York.
33. K. Shibata, Y. Ikeda, Effect of number of hidden neurons on learning in large-scale layered neural networks, in: ICROS-SICE International Joint Conference, Fukuoka International Congress Center, Japan, August 18-21, 2009, pp. 5008-5013.
34. Lippmann R.P. An introduction to computing with neural nets. IEEE ASSP Mag. 1987, 4(April):4-22.
35. N. Wanas, G. Auda, M.S. Kamel, F. Karray, On the optimal number of hidden nodes in a neural network, in: IEEE Canadian Conference on Electrical and Computer Engineering, vol. 2, 24-28 May 1998, pp. 918-921.
36. Prentice A.M., Jebb S.A. Beyond body mass index. Obes. Rev. 2001, 2:141-147.
37. Wells J.C.K., Victora C.G. Indices of whole-body and central adiposity for evaluating the metabolic load of obesity. Int. J. Obes. 2005, 29:483-489.
38. 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. 2009, 16(6):729-734.
39. 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. 2002, 53:538-549.
40. 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) 2010, 11(3):207-220.
41. 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. 2012, 12:3.
42. 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. 2007, 28(7):850-856.
43. Bose S., Krishnamoorthy P., Varanasi A., Nair J., Schutta M., Braunstein S., Iqbal N., Schwartz S., Clair C.S., 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 2013, 21(1):E118-23.
44. 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. 2010, 23:247-269.
45. 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. 2010, 134(9):386-391.
46. 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. 2011, 26(1):13-21.
47. 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. 2012, 13(3):275-286.
48. Kupusinac A., Stokić E., Srdić B. Determination of WHtR limit for predicting hyperglycemia in obese persons by using artificial neural networks. TEM J. 2012, 1(4):270-272.
49. 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. 2005, 26(20):2120-2126.
50. 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 2011, 42(4):952-959.