Prediction of mortality in stroke patients using multilayer perceptron neural networks

Turkish Journal of Medical Sciences

Volumn 42, Issue 5, 2012, Pages 886-893

  Süt, Necdet ^a   ÇelIk, Yahya ^a  

^a TRAKYA UNIVERSITY   (Turkey)

Author keywords

Algorithm; Mortality; Multilayer perceptron neural networks; Stroke

Indexed keywords

ACCURACY; ADULT; AGE; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACKPROPAGATION; CONJUGATE GRADIENT DESCENT; DELTA BAR DELTA; DIABETES MELLITUS; EMBOLISM; FEMALE; GENDER; HEART ATRIUM FIBRILLATION; HOSPITALIZATION; HUMAN; HYPERTENSION; INFECTION; ISCHEMIC HEART DISEASE; LEVENBERG MARQUARDT; MAJOR CLINICAL STUDY; MALE; MORTALITY; MULTILAYER PERCEPTRON NEURAL NETWORK; PREDICTION; PROGNOSIS; QUASI NEWTON; QUICK PROPAGATION; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; STROKE;

EID: 84864083161     PISSN: 13000144     EISSN: None     Source Type: Journal    
DOI: 10.3906/sag-1105-20     Document Type: Article

References (18)

1. American Stroke Association. About stroke. Dallas (TX): ASA. Available from URL: http://www.strokeassociation.org/ STROKEORG/AboutStroke/About-Stroke_UCM_308529_ SubHomePage.jsp. Accessed 17 January 2011.
2. Yan H, Jiang Y, Zheng J, Peng C, Li Q. A multilayer perceptronbased medical decision support system for heart disease diagnosis. Expert Syst Appl 2006; 30: 272-81.
3. Süt N, Şenocak M. Ass essment of the performances of multilayer perceptron neural networks in comparison with recurrent neural networks and two statistical methods for diagnosing coronary artery disease. Expert Syst 2007; 24: 131-42.
4. Gurgen F, Guler N, Varol F. Antenatal fetal risk assessment by blood-fl ow velocity waveforms. IEEE Eng Med Biol Mag 2000; 19: 88-93.
5. Calcagno G, Staiano A, Fortunato G, Brescia-Morra V, Salvatore E, Liguori R et al. A multilayer perceptron neural networkbased approach for the identification of responsiveness to interferon therapy in multiple sclerosis patients. Inform Sciences 2010; 180: 4153-63.
6. Sun R, Wang Y. Predicting termination of atrial fi brillation based on the structure and quantification of the recurrence plot. Med Eng Phys 2008; 30: 1105-11.
7. Trtica-Majnaric L, Zekic-Susac M, Sarlija N, Vitale B. Prediction of infl uenza vaccination outcome by neural networks and logistic regression. J Biomed Inform 2010; 43: 774-81.
8. Türe M, Kurt İ, Kurum AT, Özdamar K. Comparing classification techniques for predicting essential hypertension. Expert Syst Appl 2005; 29: 583-8.
9. Marcos JV, Hornero R, Álvarez D, Campo F, Zamarrón C, López M. Utility of multilayer perceptron neural network classifi ers in the diagnosis of obstructive sleep apnoea syndrome from nocturnal oximetry. Comput Meth Prog Bio 2008; 92: 79-89.
10. İçer S, Kara S, Güven A. Comparison of multilayer perceptron training algorithms for portal venous doppler signals in the cirrhosis disease. Expert Syst Appl 2006; 31: 406-13.
11. Güler I, Übeyli ED. An expert system for detection of electrocardiographic changes in patients with partial epilepsy using wavelet-based neural networks. Expert Syst 2005; 22: 62-71.
12. Santhosh Baboo S, Kadar Shereef I. An effi cient weather forecasting system using artifi cial neural network. IJESD 2010; 1: 321-6.
13. Rumelhart DE, McClelland J, PDP Research Group. Parallel distributed processing: explorations in the microstructure of cognition. Cambridge (MA): MIT Press; 1986.
14. Bishop CM. Neural networks for pattern recognition. Oxford: Oxford University Press; 1995.
15. StatSoft. StatSoft electronic textbook. Available from URL: http://www.statsoft.com/textbook/stathome.html. Accessed 15 January 2011.
16. Güler NF, Übeyli ED, Güler İ. Recurrent neural networks employing L yapunov exponents for EGG signals classification. Expert Syst Appl 2005; 29: 506-14.
17. Rumelhart D, Hinton G, Williams R. Learning internal representations by back-propagating errors. Cambridge (MA): MIT Press; 1986.
18. Duda RO, Hart PE, Stork DG. Pattern classification. New York: Wiley, 2001.