Can neural network able to estimate the prognosis of epilepsy patients according to risk factors?

J Med Syst. 2010 Aug;34(4):541-50. doi: 10.1007/s10916-009-9267-8. Epub 2009 Mar 28.


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.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms*
  • Child
  • Decision Making, Computer-Assisted*
  • Epilepsy / diagnosis*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Neural Networks, Computer*
  • Prognosis
  • Risk Factors
  • Young Adult