An improved artificial neural network based model for prediction of late onset heart failure

Life Science Journal

Volumn 9, Issue 4, 2012, Pages 3684-3689

  Salari, Nader ^a,b   Shohaimi, Shamarina ^a   Najafi, Farid ^b   Nallappan, Meenakshii ^a   Karishnarajah, Isthrinayagy ^a  

^a UNIVERSITY PUTRA MALAYSIA   (Malaysia)

^b KERMANSHAH UNIVERSITY OF MEDICAL SCIENCES   (Iran)

Author keywords

Acute myocardial infarction; Artificial neural network; Heart failure; Late onset heart failure; Multilayer perceptron; Radial basis function

Indexed keywords

EID: 84877053744     PISSN: 10978135     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (25)

1. Nadar, S., Prasad, N., Taylor, R.S., Lip, G.Y.H. Positive pressure ventilation in the management of acute and chronic cardiac failure: a systematic review and meta-analysis. International journal of cardiology, 2005. 99(2): p. 171-185.
2. Bordier, P. Sleep apnoea in patients with heart failure. Part I: Diagnosis, definitions, prevalence, pathophysiology and haemodynamic consequences. Archives of Cardiovascular Diseases, 2009. 102(8-9): p. 651-661.
3. Cohn, J.N., Ferrari, R., Sharpe, N. Cardiac remodeling--concepts and clinical implications: a consensus paper from an international forum on cardiac remodeling* 1.Journal of the American College of Cardiology, 2000. 35(3): p. 569-582.
4. Gheorghiade, M., Pang, P.S. Acute Heart Failure Syndromes. Journal of the American College of Cardiology, 2009. 53(7): p. 557-573.
5. Barlow, P., Teasdale, G., Jennett, B., Murray, L., Duff, C., Murray, G. Computer-assisted prediction of outcome of severely head-injured patients. Journal of microcomputer applications, 1984. 7(3-4): p. 271-277.
6. Fabbri, A., Servadei, F., Marchesini, G., Stein, S., Vandelli, A. Early predictors of unfavourable outcome in subjects with moderate head injury in the emergency department. Journal of Neurology, Neurosurgery & Psychiatry, 2008. 79(5): p. 567.
7. Hsu, M.H., Li, Y.C., Chiu, W.T., Yen, J.C. Outcome prediction after moderate and severe head injury using an artificial neural network. Studies in health technology and informatics, 2005, 116: p. 241.
8. Lang, E.W., Pitts, L.H., Damron, S.L., Rutledge, R. Outcome after severe head injury: an analysis of prediction based upon comparison of neural network versus logistic regression analysis. Neurological research, 1997. 19(3): p. 274.
9. Pang, B.C., Kuralmani, V., Joshi, R., Hongli, Y., Lee, K.K., Ang, B.T., Li, J., Leong, T.Y., Ng, I. Hybrid outcome prediction model for severe traumatic brain injury. Journal of Neurotrauma, 2007. 24(1): p. 136-146.
10. Smits, M., Dippel, D.W.J., Nederkoorn, P.J., Dekker, H.M., Vos, P.E., Kool, D.R., van Rijssel, D.A., Hofman, P.A.M., Twijnstra, A., Tanghe, H.L.J. Minor Head Injury: CT-based Strategies for Management-A Cost-effectiveness Analysis1. Radiology, 2010. 254(2): p. 532.
11. Li, Y.C., Liu, L., Chiu, W.T., Jian, W.S. Neural network modeling for surgical decisions on traumatic brain injury patients. International journal of medical informatics, 2000. 57(1): p. 1-9.
12. Hamner, J.B., Ellison, K.J. Predictors of hospital readmission after discharge in patients with congestive heart failure. Heart & Lung: The Journal of Acute and Critical Care, 2005. 34(4): p. 231-239.
13. Najafi, F., Dobson, A.J., Hobbs, M., Jamrozik, K. Temporal trends in the frequency and longer-term outcome of heart failure complicating myocardial infarction. European journal of heart failure, 2007. 9(9): p. 879.
14. Najafi, F., Dobson, A.J., Hobbs, M., Jamrozik, K. Late-onset heart failure after myocardial infarction: Trends in incidence and survival. European journal of heart failure, 2008. 10(8): p. 765.
15. Eggers, K.M., Ellenius, J., Dellborg, M., Groth, T., Oldgren, J., Swahn, E., Lindahl, B. Artificial neural network algorithms for early diagnosis of acute myocardial infarction and prediction of infarct size in chest pain patients. International journal of cardiology, 2007. 114(3): p. 366-374.
16. Stone, P.H., Raabe, D.S., Jaffe, A.S., Gustafson, N., Muller, J.E., Turi, Z.G., Rutherford, J.D., Poole, W.K., Passamani, E., Willerson, J.T., Sobel, B.E., Robertson, T., Braunwald, E. Prognostic significance of location and type of myocardial infarction: Independent adverse outcome associated with anterior location. Journal of the American College of Cardiology, 1988. 11(3): p. 453-463.
17. Tunstall-Pedoe, H., Kuulasmaa, K., Amouyel, P., Arveiler, D., Rajakangas, A.M., Pajak, A. Myocardial infarction and coronary deaths in the World Health Organization MONICA Project. Registration procedures, event rates, and casefatality rates in 38 populations from 21 countries in four continents. Circulation, 1994. 90(1): p. 583-612.
18. Holman, C.D.A.J., Bass, A.J., Rouse, I.L., Hobbs, M.S.T. Population based linkage of health records in Western Australia: development of a health services research linked database. Australian and New Zealand Journal of Public Health, 1999. 23(5): p. 453-459.
19. Holman CD, Bass AJ, Rouse IL, Hobbs MS Population-based linkage of health records in Western Australia: development of a health services research linked database.1999. 23: p. 453-9.
20. Gallant, A.R., White, H. On learning the derivatives of an unknown mapping with multilayer feedforward networks. Neural Networks, 1992. 5(1): p. 129-138.
21. Taylor, J. Neural networks and their applications. Computers & Mathematics with Applications, 1997; 33(5): p. 131.
22. Billings, S.A., Zheng, G.L. Radial basis function network configuration using genetic algorithms. Neural Networks, 1995. 8(6): p. 877-890.
23. Pedrycz, W. Conditional fuzzy clustering in the design of radial basis function neural networks. Neural Networks, IEEE Transactions on, 1998. 9(4): p. 601-612.
24. Kubat, M. Decision trees can initialize radial-basis function networks. Neural Networks, IEEE Transactions on, 1998. 9(5): p. 813-821.
25. Moody, J., Darken, C. Learning with localized receptive fields. 1988. Morgan Kaufmann.