Key result
Support vector machine (SVM) trained on composite features and modified mixture of experts (MME) trained on diverse features achieved higher accuracy rates than other automated diagnostic systems.
Why the study?
Do automated diagnostic systems improve the capability of classification of ophthalmic arterial and internal carotid arterial Doppler signals?
Do automated diagnostic systems improve the capability of classification of ophthalmic arterial and internal carotid arterial Doppler signals?
Support vector machine and modified mixture of experts classifiers demonstrate superior accuracy in classifying ophthalmic and internal carotid arterial Doppler ultrasound signals.
May aid Doppler signal classification; leaves open whether accuracy gains improve clinical decisions in vascular diagnostics.
In this paper, we present the automated diagnostic systems for Doppler ultrasound signals classification with diverse and composite features and determine their accuracies. We compared the classification accuracies of six different classifiers, namely multilayer perceptron neural network (MLP), combined neural network (CNN), mixture of experts (ME), modified mixture of experts (MME), probabilistic neural network (PNN), and support vector machine (SVM), which were trained on diverse or composite features. The present study was conducted with the purpose of answering the question of whether the automated diagnostic systems improve the capability of classification of ophthalmic arterial (OA) and internal carotid arterial (ICA) Doppler signals. Our research demonstrated that the SVM trained on composite feature and the MME trained on diverse features achieved accuracy rates which were higher than that of the other automated diagnostic systems.
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Güler et al. (2006) studied Ophthalmic arterial (OA) and internal carotid arterial (ICA) Doppler signals. Automated diagnostic systems (SVM and MME) vs. Other classifiers (MLP, CNN, ME, PNN) was evaluated on Classification accuracy. Support vector machine (SVM) trained on composite features and modified mixture of experts (MME) trained on diverse features achieved higher accuracy rates than other automated diagnostic systems.
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