Novel classification approach to minimize the false detection rate of epilepsy using decision tree classifier in comparison with artificial neural networks.

Aim: The objective of this research is to diagnose epileptic seizures in Electroencephalogram signals using a hybrid framework novel classification Approach based on a decision tree classifier. The identification of epileptic discharges in the Electroencephalogram is a key component of the epilepsy diagnosis. Materials & Methods : The total of 1372 samples are collected from the UCI repository. The epilepsy detection is carried out with the help of two groups where group 1 is Decision Tree and group 2 is Artificial Neural Networks. Result: Decision Tree Achieved 97.9% respectively compared to 88% by Artificial Neural Networks. The obtained significant value is (P<0.05). Conclusion: It is concluded that Decision Tree has significantly greater accuracy when compared with the Artificial Neural Networks. [ABSTRACT FROM AUTHOR]