A hybrid FAM–CART model and its application to medical data classification

Seera,M, Lim,CP, Tan,SC and Loo,CK 2015, A hybrid FAM–CART model and its application to medical data classification, Neural Computing and Applications, doi: 10.1007/s00521-015-1852-9.

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Title A hybrid FAM–CART model and its application to medical data classification
Author(s) Seera,M
Lim,CPORCID iD for Lim,CP orcid.org/0000-0003-4191-9083
Journal name Neural Computing and Applications
Publisher Springer Verlag
Publication date 2015-02
ISSN 0941-0643
Keyword(s) Classification and regression tree
Decision support
Pattern classification
Rule extraction
Stability–plasticity dilemma
Summary In this paper, a hybrid model consisting of the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) is formulated. FAM is useful for tackling the stability–plasticity dilemma pertaining to data-based learning systems, while CART is useful for depicting its learned knowledge explicitly in a tree structure. By combining the benefits of both models, FAM–CART is capable of learning data samples stably and, at the same time, explaining its predictions with a set of decision rules. In other words, FAM–CART possesses two important properties of an intelligent system, i.e., learning in a stable manner (by overcoming the stability–plasticity dilemma) and extracting useful explanatory rules (by overcoming the opaqueness issue). To evaluate the usefulness of FAM–CART, six benchmark medical data sets from the UCI repository of machine learning and a real-world medical data classification problem are used for evaluation. For performance comparison, a number of performance metrics which include accuracy, specificity, sensitivity, and the area under the receiver operation characteristic curve are computed. The results are quantified with statistical indicators and compared with those reported in the literature. The outcomes positively indicate that FAM–CART is effective for undertaking data classification tasks. In addition to producing good results, it provides justifications of the predictions in the form of a decision tree so that domain users can easily understand the predictions, therefore making it a useful decision support tool.
Language eng
DOI 10.1007/s00521-015-1852-9
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Springer Verlag
Persistent URL http://hdl.handle.net/10536/DRO/DU:30074086

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