A decision support system based on support vector machines for diagnosis of the heart valve diseases
Computers in Biology and Medicine
A biomedical system based on hidden Markov model for diagnosis of the heart valve diseases
Pattern Recognition Letters
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Effective diagnosis of heart disease through neural networks ensembles
Expert Systems with Applications: An International Journal
A comparison of multiple classification methods for diagnosis of Parkinson disease
Expert Systems with Applications: An International Journal
Evaluation of ensemble methods for diagnosing of valvular heart disease
Expert Systems with Applications: An International Journal
Classification of pulmonary nodules using neural network ensemble
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions
Journal of Medical Systems
A survey of multiple classifier systems as hybrid systems
Information Fusion
Indirect immunofluorescence image classification using texture descriptors
Expert Systems with Applications: An International Journal
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In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the valvular heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS Base Software 9.1.3 for diagnosing of the valvular heart disease. A neural networks ensemble method is in the centre of the proposed system. The ensemble-based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with proposed tool. We obtained 97.4% classification accuracy from the experiments made on data set containing 215 samples. We also obtained 100% and 96% sensitivity and specificity values, respectively, in valvular heart disease diagnosis.