The long-term solution to the asthma epidemic is believed to be prevention and not treatment of the established disease. Most cases of asthma begin during the first years of life; thus the early determination of which young children will have asthma later in their life counts as an important priority. Artificial neural networks (ANN) have been already utilized in medicine in order to improve the performance of the clinical decision-making tools. In this study, a new computational intelligence technique for the prediction of persistent asthma in children is presented. By employing partial least square regression, 9 out of 48 prognostic factors correlated to the persistent asthma have been chosen. Multilayer perceptron and probabilistic neural networks topologies have been investigated in order to obtain the best prediction accuracy. Based on the results, it is shown that the proposed system is able to predict the asthma outcome with a success of 96.77%. The ANN, with which these high rates of reliability were obtained, will help the doctors to identify which of the young patients are at a high risk of asthma disease progression. Moreover, this may lead to better treatment opportunities and hopefully better disease outcomes in adulthood. 1. Introduction Artificial neural networks (ANNs) are one of the main constituents of the artificial intelligence (AI) techniques. Besides the different applications in many other areas, neural networks are also used in health and medicine areas, such as biomedical signal processing, diagnosis of diseases, and medical decision [1, 2]. ANNs have an excellent capability of learning the relationship between the input-output mapping from a given dataset without any prior knowledge or assumptions about the statistical distribution of the data [3]. This capability of learning from a certain dataset without any a priori knowledge makes the neural networks suitable for classification and prediction tasks in practical situations. Furthermore, neural networks are inherently nonlinear which makes them more practicable for accurate modeling of complex data patterns, in contrast to many traditional methods based on linear techniques. Due to their performance, they can be applied in a wide range of medical fields such as cardiology, gastroenterology, pulmonology, oncology, neurology, and pediatrics [1]. Several studies have proposed ANN models for the prediction of various diseases. The authors of [4] developed an ANN to determine whether patients had breast cancer or not. If they had, its type could be determined by using ANN and
References
[1]
W. G. Baxt, “Application of artificial neural networks to clinical medicine,” The Lancet, vol. 346, no. 8983, pp. 1135–1138, 1995.
[2]
E. Sourla, S. Sioutas, V. Syrimpeis, A. Tsakalidis, and G. Tzimas, “CardioSmart365: artificial intelligence in the service of cardiologic patients,” Advances in Artificial Intelligence, vol. 2012, Article ID 585072, 12 pages, 2012.
[3]
P. S. Heckerling, B. S. Gerber, T. G. Tape, and R. S. Wigton, “Entering the black box of neural networks: a descriptive study of clinical variables predicting community-acquired pneumonia,” Methods of Information in Medicine, vol. 42, no. 3, pp. 287–296, 2003.
[4]
I. Saritas, “Prediction of breast cancer using artificial neural networks,” Journal of Medical Systems, vol. 36, no. 5, pp. 2901–2907, 2012.
[5]
F. Feng, Y. Wu, Y. Wu, G. Nie, and R. Ni, “The effect of artificial neural network model combined with six tumor markers in auxiliary diagnosis of Lung Cancer,” Journal of Medical Systems, vol. 36, no. 5, pp. 2973–2980, 2012.
[6]
D. F. Specht and P. D. Shapiro, “Generalization accuracy of probabilistic neural networks compared with back-propagation networks,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '91), pp. 887–892, July 1991.
[7]
W. Eder, M. J. Ege, and E. Von Mutius, “The asthma epidemic,” New England Journal of Medicine, vol. 355, no. 21, pp. 2226–2235, 2006.
[8]
A. Bush, “Diagnosis of asthma in children under five,” Primary Care Respiratory Journal, vol. 16, no. 1, pp. 7–15, 2007.
[9]
W. J. Morgan, D. A. Stern, D. L. Sherrill et al., “Outcome of asthma and wheezing in the first 6 years of life follow-up through adolescence,” American Journal of Respiratory and Critical Care Medicine, vol. 172, no. 10, pp. 1253–1258, 2005.
[10]
F. D. Martinez, A. L. Wright, L. M. Taussig et al., “Asthma and wheezing in the first six years of life,” New England Journal of Medicine, vol. 332, no. 3, pp. 133–138, 1995.
[11]
M. B. Bracken, K. Belanger, W. O. Cookson, E. Triche, D. C. Christiani, and B. P. Leaderer, “Genetic and perinatal risk factors for asthma onset and severity: a review and theoretical analysis,” Epidemiologic Reviews, vol. 24, no. 2, pp. 176–189, 2002.
[12]
N. E. Lange, S. L. Rifas-Shiman, C. A. Camargo, D. R. Gold, M. W. Gillman, and A. A. Litonjua, “Maternal dietary pattern during pregnancy is not associated with recurrent wheeze in children,” Journal of Allergy and Clinical Immunology, vol. 126, no. 2, pp. 250–e4, 2010.
[13]
G. Nagel, G. Weinmayr, A. Kleiner et al., “Effect of diet on asthma and allergic sensitisation in the international study on allergies and asthma in childhood (ISAAC) phase two,” Thorax, vol. 65, no. 6, pp. 516–522, 2010.
[14]
C. Porsbjerg, M. L. Von Linstow, C. S. Ulrik, S. Nepper-Christensen, and V. Backer, “Risk factors for onset of asthma: a 12-year prospective follow-up study,” Chest, vol. 129, no. 2, pp. 309–316, 2006.
[15]
B. G. Toelle, W. Xuan, J. K. Peat, and G. B. Marks, “Childhood factors that predict asthma in young adulthood,” European Respiratory Journal, vol. 23, no. 1, pp. 66–70, 2004.
[16]
W. Balemansa, C. Van der Enta, A. Schilderb, E. Sandersc, G. Zielhuisd, and M. Roversbef, “Prediction of asthma in young adults using childhood characteristics: development of a prediction rule,” Journal of Clinical Epidemiology, vol. 59, no. 11, pp. 1207–1212, 2006.
[17]
C. Bodner, S. Ross, G. Douglas et al., “The prevalence of adult onset wheeze: longitudinal study,” British Medical Journal, vol. 314, no. 7083, pp. 792–793, 1997.
[18]
J. A. Castro-Rodríguez, C. J. Holberg, A. L. Wright, and F. D. Martinez, “A clinical index to define risk of asthma in young children with recurrent wheezing,” American Journal of Respiratory and Critical Care Medicine, vol. 162, no. 4, pp. 1403–1406, 2000.
[19]
J. A. Castro-Rodriguez, “The asthma predictive index: a very useful tool for predicting asthma in young children,” Journal of Allergy and Clinical Immunology, vol. 126, no. 2, pp. 212–216, 2010.
[20]
J. Yang, A. S. Nugroho, K. Yamauchi et al., “Efficacy of interferon treatment for chronic hepatitis C predicted by feature subset selection and support vector machine,” Journal of Medical Systems, vol. 31, no. 2, pp. 117–123, 2007.
[21]
C. L. Chang and C. H. Chen, “Applying decision tree and neural network to increase quality of dermatologic diagnosis,” Expert Systems with Applications, vol. 36, no. 2, pp. 4035–4041, 2009.
[22]
J. Xia, X. Hu, F. Shi, X. Niu, and S. Zhang, “Prediction of disease-resistant gene by using artificial neural network,” in Proceedings of the International Conference on Research Challenges in Computer Science (ICRCCS '09), pp. 81–84, December 2009.
[23]
A. Coster and M. P. L. Calus, “Partial least square regression applied to the QTLMAS, 2010 dataset,” BMC Proceedings, vol. 5, 3, p. S7, 2011.
[24]
G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of Control, Signals, and Systems, vol. 2, no. 4, pp. 303–314, 1989.
[25]
E. Patuwo, M. Y. Hu, and M. S. Hung, “Two-group classification using neural networks,” Decision Sciences, vol. 24, no. 4, pp. 825–845, 1993.
[26]
D. Howard and B. Mark, Neural Network Toolbox for Use With Matlab, The Mathworks, Natick, Mass, USA, 2004.
[27]
E. Chatzimichail, A. Rigas, E. Paraskakis, and A. Chatzimichail, “Diagnosis of asthma severity using artificial neural networks,” in Proceedings of the 12th Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON '10), pp. 600–603, Chalkidiki, Greece, May 2010.
[28]
J. Dheeba and T. Selvi, “A swarm optimized neural network system for classification of microcalcification in mammograms,” Journal of Medical Systems, vol. 36, no. 5, pp. 3051–3061, 2012.
[29]
I. Kononenko, “Inductive and Bayesian learning in medical diagnosis,” Applied Artificial Intelligence, vol. 7, no. 4, pp. 317–337, 1993.
[30]
W. Wongseree, N. Chaiyaratana, K. Vichittumaros, P. Winichagoon, and S. Fucharoen, “Thalassaemia classification by neural networks and genetic programming,” Information Sciences, vol. 177, no. 3, pp. 771–786, 2007.
[31]
J. Chiu, Y. Wang, Y. Su, L. Wei, and J. Liao, “Artificial neural network to predict skeletal metastasis in patients with prostate cancer,” Journal of Medical Systems, vol. 33, no. 2, pp. 91–100, 2009.
[32]
W. Ji, R. N. G. Naguib, and M. A. Ghoneim, “Neural network-based assessment of prognostic markers and outcome prediction in Bilharziasis-associated bladder cancer,” IEEE Transactions on Information Technology in Biomedicine, vol. 7, no. 3, pp. 218–224, 2003.
