Implementation of K nearest neighbour algorithm to minimize the false detection rate of epilepsy using novel classification approach in comparison with artificial neural network algorithm.

This paper describes an novel classification approach of EEG signals for the detection of epileptic seizures using K-Nearest Neighbour and Artificial Neural Networks. : The total of 1372 samples are collected from the UCI repository which contain 15 attributes. The epilepsy detection is carried out with the help of two groups where group 1 is K Nearest Neighbour and group 2 is Artificial Neural Networks. K Nearest Neighbour Achieved 97% respectively compared to 88% by Artificial Neural Networks. The obtained significant value is (p<0.05). We can conclude that K-Nearest Neighbor has significantly greater accuracy when compared with the Artificial Neural Networks. [ABSTRACT FROM AUTHOR]