J Biomed Inform - A medical diagnostic tool based on radial basis function classifiers and evolutionary simulated annealing.


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JECTIVE: 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.

Resumo Limpo

jectiv profus data accumul form medic record great help develop medic decis support system object paper present methodolog design datadriven medic diagnost tool base neural network classifiersmethod propos approach adopt radial basi function rbf neural network architectur nonsymmetr fuzzi mean nsfm train algorithm present certain advantag includ better approxim capabl shorter comput time novelti work consist adapt nsfm algorithm train rbf classifi suitabl tailor evolutionari simul anneal esa techniqu optim produc rbf model integr esa critic help optim procedur escap local minima aris applic tradit simul anneal algorithm thus discov improv solut result method evalu nine differ medic benchmark dataset common object train suitabl classifi evalu includ comparison two differ scheme train classifi includ standard rbf train techniqu support vector machin svms accuraci matthew correl coeffici mcc use compar perform three classifiersresult result show use esa help great improv perform nsfm algorithm provid satisfactori classif accuraci almost benchmark dataset best solut found esansfm algorithm outperform result produc sfm algorithm svms consid either accuraci mcc criterion furthermor major dataset averag solut esansfm popul statist signific higher term accuraci mcc confid level compar global optimum solut rival achiev far comput time concern propos approach found faster compar svmsconclus result studi suggest esansfm algorithm can form basi generic method knowledg extract data origin differ kind medic record test propos approach number benchmark dataset indic provid increas diagnost accuraci comparison two differ classifi train method

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