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Differentiation of Two Subtypes of Adult Hydrocephalus by Mixture of Experts

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Abstract

This paper illustrates the use of mixture of experts (ME) network structure to guide model selection for diagnosis of two subtypes of adult hydrocephalus (normal-pressure hydrocephalus–NPH and aqueductal stenosis–AS). The ME is a modular neural network architecture for supervised learning. Expectation-Maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. To improve classification accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The classifiers were trained on the defining features of NPH and AS (velocity and flux). Three types of records (normal, NPH and AS) were classified with the accuracy of 95.83% by the ME network structure. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji Üniversitesi, 06530 Söğütözü, Ankara, Turkey

    Elif Derya Übeyli

  2. Departmant of Neurosurgery, Faculty of Medicine, Kocaeli University, Umut Tepe Campus, 31380, Kocaeli, Turkey

    Konuralp Ilbay

  3. Departmant of Physiology, Faculty of Medicine, Kocaeli University, Umut Tepe Campus, 31380, Kocaeli, Turkey

    Gul Ilbay & Deniz Sahin

  4. Departmant of Radiology, Faculty of Medicine, Kocaeli University, Umut Tepe Campus, 31380, Kocaeli, Turkey

    Gur Akansel

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  1. Elif Derya Übeyli
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Correspondence to Elif Derya Übeyli.

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Übeyli, E.D., Ilbay, K., Ilbay, G. et al. Differentiation of Two Subtypes of Adult Hydrocephalus by Mixture of Experts. J Med Syst 34, 281–290 (2010). https://doi.org/10.1007/s10916-008-9239-4

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