Abstract
The paper presents the research on the computer support system which is able to recognize the type of hypertension. This diagnostic problem is highly imbalanced, because only ca. 5% of patient suffering from hypertension are diagnosed as secondary hypertension. Additionally the secondary hypertension could be caused by several disorders (in our work we recognize the five most popular reasons) which require strikingly different therapies. Thus, appropriate classification methods, which take into consideration the nature of the decision task should be applied to this problem. We decided to employ the original classification methods developed by our team which have their origin in one-class classification and the ensemble learning. They quality was confirmed in our previous works. The accuracy of the chosen classifiers was evaluated on the basis of the computer experiments which were carried out on the real data set obtained from the hypertension clinic. The results of the experimental investigations confirmed usefulness of the proposed, hierarchical one-class classifier ensemble and could be applied in the real medical decision support systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Liebowitz, J.: The Handbook of Applied Expert Systems. Taylor & Francis (1997), http://books.google.pl/books?id=6DNgIzFNSZsC
Wozniak, M.: Two-stage classifier for diagnosis of hypertension type. In: Maglaveras, N., Chouvarda, I., Koutkias, V., Brause, R. (eds.) ISBMDA 2006. LNCS (LNBI), vol. 4345, pp. 433–440. Springer, Heidelberg (2006)
Mancia, G., et al.: esh/esc guidelines for the management of arterial hypertension: the task force for the management of arterial hypertension of the european society of hypertension (esh) and of the european society of cardiology (esc). European Heart Journal 34(28), 2159–2219 (2013)
Lloyd-Jones, D., Adams, R.J., Brown, T.M., Carnethon, M., Dai, S., De Simone, G., Ferguson, T.B., Ford, E., Furie, K., Gillespie, C., et al.: Heart disease and stroke statistics 2010 update a report from the american heart association. Circulation 121(7), e46–e215 (2010)
Sun, Y., Wong, A.K.C., Kamel, M.S.: Classification of imbalanced data: A review. International Journal of Pattern Recognition and Artificial Intelligence 23(4), 687–719 (2009)
Chen, X., Wasikowski, M.: Fast: A roc-based feature selection metric for small samples and imbalanced data classification problems. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 124–132 (2008)
Garcia, V., Mollineda, R.A., Sánchez, J.S.: On the k-nn performance in a challenging scenario of imbalance and overlapping. Pattern Analysis and Applications 11(3-4), 269–280 (2008)
Napierala, K., Stefanowski, J.: Identification of different types of minority class examples in imbalanced data. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012, Part II. LNCS (LNAI), vol. 7209, pp. 139–150. Springer, Heidelberg (2012)
Lopez, V., Fernandez, A., Moreno-Torres, J.G., Herrera, F.: Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. open problems on intrinsic data characteristics. Expert Systems with Applications 39(7), 6585–6608 (2012)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., Herrera, F.: A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 42(4), 463–484 (2012)
Chawla, N.V., Lazarevic, A., Hall, L.O., Bowyer, K.W.: Smoteboost: Improving prediction of the minority class in boosting. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 107–119. Springer, Heidelberg (2003)
Liu, X., Wu, J., Zhou, Z.: Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39(2), 539–550 (2009)
Sun, Y., Kamel, M.S., Wong, A.K.C., Wang, Y.: Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition 40(12), 3358–3378 (2007)
Tax, D.M.J., Duin, R.P.W.: Support vector data description. Machine Learning 54(1), 45–66 (2004)
Tax, D., Duin, R.P.W.: Characterizing one-class datasets. In: Proceedings of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, pp. 21–26 (2005)
Giacinto, G., Perdisci, R., Del Rio, M., Roli, F.: Intrusion detection in computer networks by a modular ensemble of one-class classifiers. Inf. Fusion 9, 69–82 (2008)
Krawczyk, B., Woźniak, M.: Diversity measures for one-class classifier ensembles. Neurocomputing 126, 36–44 (2014)
Krawczyk, B., Woźniak, M., Cyganek, B.: Clustering-based ensembles for one-class classification. Information Sciences 264, 182–195 (2014)
Wilk, T., Wozniak, M.: Soft computing methods applied to combination of one-class classifiers. Neurocomput. 75, 185–193 (2012)
Alpaydin, E.: Combined 5 x 2 cv f test for comparing supervised classification learning algorithms. Neural Computation 11(8), 1885–1892 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Krawczyk, B., Woźniak, M. (2015). Hypertension Type Classification Using Hierarchical Ensemble of One-Class Classifiers for Imbalanced Data. In: Bogdanova, A., Gjorgjevikj, D. (eds) ICT Innovations 2014. ICT Innovations 2014. Advances in Intelligent Systems and Computing, vol 311. Springer, Cham. https://doi.org/10.1007/978-3-319-09879-1_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-09879-1_34
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09878-4
Online ISBN: 978-3-319-09879-1
eBook Packages: EngineeringEngineering (R0)