A medical diagnostic tool based on radial basis function classifiers and evolutionary simulated annealing

dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Χονδροδήμα, Ευαγγελία	el
dc.date.accessioned	2015-06-03T18:23:31Z
dc.date.available	2015-06-03T18:23:31Z
dc.date.issued	2015-06-03
dc.identifier.uri	http://hdl.handle.net/11400/15002
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.elsevier.com/	en
dc.subject	Decision support systems
dc.subject	Evolutionary computation
dc.subject	Medical diagnosis
dc.subject	Neural networks
dc.subject	Radial basis functions
dc.subject	Simulated annealing
dc.subject	υστήματα υποστήριξης αποφάσεων
dc.subject	Εξελικτική υπολογιστική
dc.subject	Ιατρική διάγνωση
dc.subject	Νευρωνικά δίκτυα
dc.subject	Ακτινική συνάρτηση βάσης
dc.subject	Προσομοιωμένη ανόπτηση
dc.title	A medical diagnostic tool based on radial basis function classifiers and evolutionary simulated annealing	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Electrical engineering
heal.classification	Medicine
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρολογία Μηχανολογία
heal.classification	Ιατρική
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://zbw.eu/stw/descriptor/18426-4
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρολογία Μηχανολογία
heal.classificationURI	N/A-Ιατρική
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh86006549
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh95003989
heal.keywordURI	http://zbw.eu/stw/descriptor/19808-6
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2002004691
heal.identifier.secondary	DOI: 10.1016/j.jbi.2014.03.008
heal.language	en
heal.access	campus
heal.publicationDate	2014-06
heal.bibliographicCitation	ALEXANDRIDIS, A.P. & CHONDRODIMA, E. (2014). A medical diagnostic tool based on radial basis function classifiers and evolutionary simulated annealing. Journal of Biomedical Informatics. [online] 49. p. 61-72. Available from: http://www.elsevier.com/[Accessed 21/03/2014]	en
heal.abstract	Objective: The profusion of data accumulating in the form of medical records could be of great help for developing medical decision support systems. The objective of this paper is to present a methodology for designing data-driven medical diagnostic tools, based on neural network classifiers. Methods: The proposed approach adopts the radial basis function (RBF) neural network architecture and the non-symmetric fuzzy means (NSFM) training algorithm, which presents certain advantages including better approximation capabilities and shorter computational times. The novelty in this work consists of adapting the NSFM algorithm to train RBF classifiers, and suitably tailoring the evolutionary simulated annealing (ESA) technique to optimize the produced RBF models. The integration of ESA is critical as it helps the optimization procedure to escape from local minima, which could arise from the application of the traditional simulated annealing algorithm, and thus discover improved solutions. The resulting method is evaluated in nine different medical benchmark datasets, where the common objective is to train a suitable classifier. The evaluation includes a comparison with two different schemes for training classifiers, including a standard RBF training technique and support vector machines (SVMs). Accuracy% and the Matthews Correlation Coefficient (MCC) are used for comparing the performance of the three classifiers. Results: Results show that the use of ESA helps to greatly improve the performance of the NSFM algorithm and provide satisfactory classification accuracy. In almost all benchmark datasets, the best solution found by the ESA-NSFM algorithm outperforms the results produced by the SFM algorithm and SVMs, considering either the accuracy% or the MCC criterion. Furthermore, in the majority of datasets, the average solution of the ESA-NSFM population is statistically significantly higher in terms of accuracy% and MCC at the 95% confidence level, compared to the global optimum solution that its rivals could achieve. As far as computational times are concerned, the proposed approach was found to be faster compared to SVMs. Conclusions: The results of this study suggest that the ESA-NSFM algorithm can form the basis of a generic method for knowledge extraction from data originating from different kinds of medical records. Testing the proposed approach on a number of benchmark datasets, indicates that it provides increased diagnostic accuracy in comparison with two different classifier training methods.	en
heal.publisher	Elsevier	en
heal.journalName	Journal of Biomedical Informatics	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true