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

• Published in: Journal of biomedical informatics Vol. 49; pp. 61 - 72
• Main Authors: Alexandridis, Alex, Chondrodima, Eva
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.06.2014
• Subjects: Accuracy; Algorithms; Benchmarking; Classifiers; Decision support systems; Diagnosis, Computer-Assisted; Evolutionary computation; Fuzzy Logic; Humans; Mathematical models; Medical; Medical diagnosis; Neural networks; Neural Networks (Computer); Radial basis function; Simulated annealing; Training; Decision support systems; Radial basis function; Medical diagnosis; Neural networks; Evolutionary computation; Simulated annealing
• ISSN: 1532-0464; 1532-0480; 1532-0480
• DOI: 10.1016/j.jbi.2014.03.008

[Display omitted] •We present a method for developing neural network classifiers for medical diagnosis.•We use radial basis function networks trained with non-symmetric fuzzy means.•The classifiers are optimized using evolutionary simulated annealing (ESA).•ESA helps to escape from local minima and train classifiers with increased accuracy.•The method is evaluated successfully on nine medical diagnostic datasets. 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. 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 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. 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.