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AI InsightThe conclusions of this studydemonstrated that the modified mixture of experts trained on diverse features achieved accuracy rateswhich were higher than that of the other automated diagnostic systems trained on composite features.
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Abstract
In this study, the automated diagnostic systems employing diverse and composite features forelectrocardiogram (ECG) signals were analyzed and their accuracies were determined. In patternrecognition applications, diverse features are extracted from raw data which needs recognizing.Combining multiple classifiers with diverse features are viewed as a general problem in variousapplication areas of pattern recognition. Because of the importance of making the right decision,classification procedures classifying the ECG signals with high accuracy were analyzed. Theclassification accuracies of multilayer perceptron neural network, combined neural network, andmixture of experts trained on composite features and modified mixture of experts trained on diversefeatures were compared. The inputs of these automated diagnostic systems composed of diverse orcomposite features and were chosen according to the network structures. The conclusions of this studydemonstrated that the modified mixture of experts trained on diverse features achieved accuracy rateswhich were higher than that of the other automated diagnostic systems trained on composite features.