Abstract
Decision Support Systems (DSSs) based on fuzzy logic have gained increasing importance to help clinical decisions, since they rely on a transparent and interpretable rule base. On the other hand, probabilistic models are undoubtedly the most effective way to reach high performances. In order to join positive features of both these two approaches, this work proposes a hybrid approach, consisting in transforming the functions describing posterior probabilities, into a combination of orthogonal fuzzy sets approximating them. The resulting fuzzy partition has double hopefulness: since it approximates posterior probabilities, it is able to model information extracted from a dataset in such a form that they can be used to run predictions, and since it is a set of normal, orthogonal and convex fuzzy sets, it can be interpreted as the set of terms of a linguistic variable. As a proof of concept, the method has been applied to a real-life application pertaining the classification of Multiple Sclerosis Lesions. The results show that this method is able to construct, for each one of the variables influencing the classification, interpretable if-then rules, with classification power comparable to that of a classical Bayesian model.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Jacob, S.G., Ramani, R.G.: Mining of classification patterns in clinical data through data mining algorithms. In: Proc. of ICACCI, pp. 997–1003 (2012)
Zadeh, L.: Fuzzy sets. Inform. Control 8, 338–353 (1965)
Palit, A.K., Popovic, D.: Computational Intelligence in Time Series Forecasting - Theory and Engineering Applications, pp. 275–303 (2005)
Esposito, M., De Falco, I., De Pietro, G.: An evolutionary-fuzzy DSS for assessing health status in multiple sclerosis disease. Int. J. Med. Inf. 80, e245–e254 (2011)
Guillaume, S.: Designing fuzzy inference systems from data: an interpretability-oriented review. IEEE Trans. Fuzzy Syst. 9, 426–443 (2001)
Quinlan, J.R.: Induction on decision trees. Machine Learning 1, 81–106 (1986)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proc. 5th Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, Norwell (1981)
Yuan, Y., Shaw, M.J.: Induction of fuzzy decision trees. Fuzzy Sets and Systems 65, 125–139 (1995)
Glorennec, P.Y.: Algorithmes d’apprentissage pour systèmes d’inférence floue. Editions Hermès, Paris (1999)
Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 22, 1414–1427 (1992)
Box, G.E.P., Tiao, G.C.: Bayesian Inference in Statistical Analysis. Wiley (1973)
Akbarzadeh-T., M.-R., Moshtagh-K., M.: A hierarchical fuzzy rule-based approach for aphasia diagnosis. J. Biomedical Informatics 40, 465–475 (2007)
Schuerz, M., Adlassnig, K.-P., Lagor, C., Schneider, B., Grabner, G.: Definition of fuzzy sets representing medical concepts and acquisition of fuzzy relationships between them by semi-automatic procedures. (Electronic Newsletter) Fuzzy and Soft Computing Digest 1(2) (1999)
Pota, M., Esposito, M., De Pietro, G.: Transforming probability distributions into membership functions of fuzzy classes: A hypothesis test approach. Fuzzy Sets and Systems (2013), http://dx.doi.org/10.1016/j.fss.2013.03.013
Pota, M., Esposito, M., De Pietro, G.: From likelihood uncertainty to fuzziness: A possibility-based approach for building clinical DSSs. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012, Part II. LNCS, vol. 7209, pp. 369–380. Springer, Heidelberg (2012)
Ghazavi, S.N., Liao, T.W.: Medical data mining by fuzzy modeling with selected features. Artificial Intelligence in Medicine 43, 195–206 (2008)
Guillaume, S., Charnomordic, B.: Learning interpretable fuzzy inference systems with FisPro. Information Sciences 181, 4409–4427 (2011)
Looftsgaarden, D.O., Quesenberry, C.P.: A non-parametric estimate of a multivariate density function. The Annales of Mathematical Statistics 36, 1049–1051 (1965)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pota, M., Esposito, M., De Pietro, G. (2013). Best Fuzzy Partitions to Build Interpretable DSSs for Classification in Medicine. In: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_56
Download citation
DOI: https://doi.org/10.1007/978-3-642-40846-5_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40845-8
Online ISBN: 978-3-642-40846-5
eBook Packages: Computer ScienceComputer Science (R0)