Predicting the long-term outcome of preschool children with asthma symptoms

2011 E-Health and Bioengineering Conference, EHB 2011

Volumn , Issue , 2011, Pages

  Chatzimichail, Eleni ^a   Paraskakis, Emmanouil ^a   Sitzimi, Maria ^a   Rigas, Alexandros ^a  

^a DEMOCRITUS UNIVERSITY OF THRACE   (Greece)

Author keywords

asthma prediction; feature selection; Multi Layer Perceptron Neural Networks; Probabilistic Neural Networks

Indexed keywords

COMPUTATIONAL INTELLIGENCE TECHNIQUES; FEATURE REDUCTION; FIRST YEAR; LONG-TERM OUTCOME; LONG-TERM SOLUTIONS; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; NETWORK TOPOLOGY; OUTCOME PREDICTION; PREDICTION ACCURACY; PROBABILISTIC NEURAL NETWORKS; PROGNOSTIC FACTORS; YOUNG CHILDREN;

ELECTRIC NETWORK TOPOLOGY; FEATURE EXTRACTION; FORECASTING; HEALTH RISKS; NEURAL NETWORKS; PROBLEM SOLVING;

DISEASES;

EID: 84857831299     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. WG. Baxt, "Application of artificial neural networks to clinical medicine", Lancet, vol. 346, 1995, pp. 1135-1138.
2. DF. Specht, "Enhancements to probabilistic neural networks", in Int Joint Conference on Neural Networks, 1992, pp. 761-768.
3. W. Eder, M.J. Ege and E. Mutius, "The asthma epidemic", N Engl J Med, 2006, vol. 355, no. 21, pp. 2226-35. (Pubitemid 44791164)
4. A. Bush, "Diagnosis of asthma in children under five", Prim Care Respir J, 2007, vol.16, no. 1, pp. 7-15. (Pubitemid 46364350)
5. F. D. Martinez, A. L. Wright, L. M. Taussig, C. J. Holberg, M. Halonen and W. J.Morgan, "Asthma and wheezing in the first six years of life", N Engl J Med, 1995, vol. 332, pp. 133-138.
6. B. G. Toelle, W. Xuan and J. K. Peat, "Childhood factors that predict asthma in adulthood", Eur Respir J, 2004, vol. 23, pp. 66-70. (Pubitemid 38088498)
7. E. Chatzimichail, A. Rigas and E. Paraskakis, "An Artificial Intelligence Technique for the Prediction of Persistent Asthma in Children", in 10th IEEE International Conference on Information Technology and Applications in Biomedicin, 2010, Corfu, Greece.
8. G. Cybenko, 1989. "Approximation by superpositions of a sigmoidal function", Mathematics of Control, Signal, and System, 1989, vol. 2, pp. 303-314.
9. E. Patuwo, M.Y. Hu and M.S. Hung, "Two-group classification using neural networks", Decision Sciences, 1993, vol. 24, no. 4, pp. 825-845.
10. D. Howard, B. Mark, Neural Network Toolbox for Use with Matlab, The Mathworks Inc., 4th ed., 2004.
11. C. A. Frantzidis, C. Bratsas, C.L. Papadelis, E. Konstantinidis, C. Pappas and P. D. Bamidis, "Toward emotion aware computing: an integrated approach using multichannel neurophysiological recordings and affective visual stimuli", IEEE Trans. Information Technology in Biomedicine, 2010, vol. 14, no. 3, pp. 589-97.
12. W. Ji, R. N. Naguib and M. A. Ghoneim, " Neural network-based assessment of prognostic markers and outcome prediction in bilharziasis- associated cancer", IEEE Trans, Information Technology in Biomedicine, Sept. 2003, vol. 7, no. 3, pp. 218-224.
13. J. Michel, G. Mirchandani and S. Wald, "Prognosis with neural networks using statistically based feature sets", in Proc. 5th Annual IEEE Symposium on Computer-Based Medical Systems, Jan. 1992, pp. 695-702.
14. M. Gletsos, S. Mougiakakou, G. Matsopoulos, K. Nikita, A. Nikita and D. Kelekis, "A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier", IEEE Trans. Information Technology in Biomedicine, 2003, vol. 7, no. 3, pp. 153-162.