estimating the optimal dosage of sodium valproate in idiopathic generalized epilepsy with adaptive neuro-fuzzy inference system

introduction: epilepsy is a clinical syndrome in which seizures have a tendency to recur. sodium valproate is the most effective drug in the treatment of all types of generalized seizures. finding the optimal dosage (the lowest effective dose) of sodium valproate is a real challenge to all neurologists. in this study, a new approach based on adaptive neuro-fuzzy inference system (anfis) was presented for estimating the optimal dosage of sodium valproate in ige (idiopathic generalized epilepsy) patients. methods: 40 patients with idiopathic generalized epilepsy, who were referred to the neurology department of mashhad university of medical sciences between the years 2006-2011, were included in this study. the function adaptive neuro- fuzzy inference system (anfis) constructs a fuzzy inference system (fis) whose membership function parameters are tuned (adjusted) using either a back-propagation algorithm alone, or in combination with the least squares type of method (hybrid algorithm). in this study, we used hybrid method for adjusting the parameters. methods: the r-square of the proposed system was %598 and the pearson correlation coefficient was significant (p <0.05) and equal to 0.77, but thet-test was not significant (p >0.05). although the accuracy of the model was not high, it wasgood enough to be applied for treating the ige patients with sodium valproate. discussion: this paper presented a new application of anfis for estimating the optimal dosage of sodium valproate in ige patients. fuzzy set theory plays an important role in dealing with uncertainty when making decisions in medical applications. collectively, it seems that anfis has a high capacity to be applied in medical sciences, especially neurology.