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Can Neural Network Able to Estimate the Prognosis of Epilepsy Patients Accorrding to Risk Factors?

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Abstract

The aim of this study is to evaluate the underlying etiologic factors of epilepsy patients and to predict the prognosis of these patients by using a Multi-Layer Perceptron Neural Network (MLPNN) according to risk factors. 758 patients with epilepsy diagnosis are included in this study. The MLPNNs were trained by the parameters of demographic properties of the patients and risk factors of the disease. The results show that the most crucial risk factor of the epilepsy patients was constituted by the febrile convulsion (21.9%), the kinship of parents (22.3%), the history of epileptic relatives (21.6%) and the history of head injury (18.6%). We had 91.1 % correct prediction rate for detection of the prognosis by using the MLPNN algorithm. The results indicate that the correct prediction rate of prognosis of the MLPNN model for epilepsy diseases is found satisfactory.

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Authors and Affiliations

  1. Department of Neurology, Faculty of Medicine, Cukurova University, Adana, Turkey

    Kezban Aslan & Hacer Bozdemir

  2. Department of Industrial Engineering, Faculty of Engineering and Architecture, Cukurova University, Adana, Turkey

    Cenk Sahin & S. Noyan Ogulata

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  1. Kezban Aslan
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Aslan, K., Bozdemir, H., Sahin, C. et al. Can Neural Network Able to Estimate the Prognosis of Epilepsy Patients Accorrding to Risk Factors?. J Med Syst 34, 541–550 (2010). https://doi.org/10.1007/s10916-009-9267-8

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