caa a22 4500 606159932 CHVBK 20210128100627.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00521-015-1852-9 doi (NATIONALLICENCE)springer-10.1007/s00521-015-1852-9 A hybrid FAM-CART model and its application to medical data classification [Elektronische Daten] [Manjeevan Seera, Chee Lim, Shing Tan, Chu Loo] 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. The Natural Computing Applications Forum, 2015 Fuzzy ARTMAP nationallicence Classification and regression tree nationallicence Pattern classification nationallicence Stability-plasticity dilemma nationallicence Rule extraction nationallicence Decision support nationallicence Seera Manjeevan Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia aut Lim Chee Centre for Intelligent Systems Research, Deakin University, Geelong, VIC, Australia aut Tan Shing Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia aut Loo Chu Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia aut Neural Computing and Applications Springer London 26/8(2015-11-01), 1799-1811 0941-0643 26:8<1799 2015 26 521 https://doi.org/10.1007/s00521-015-1852-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-015-1852-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Seera Manjeevan Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia aut NATIONALLICENCE 700 1- Lim Chee Centre for Intelligent Systems Research, Deakin University, Geelong, VIC, Australia aut NATIONALLICENCE 700 1- Tan Shing Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia aut NATIONALLICENCE 700 1- Loo Chu Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/8(2015-11-01), 1799-1811 0941-0643 26:8<1799 2015 26 521