%0 Journal Article
%T Diagnosis of Parkinson¡¯s Disease by Boosted Neural Networks
%A Mehmet Can
%J Southeast Europe Journal of Soft Computing
%D 2013
%I 
%X A boosting by filtering technique for neural network systems with back propagation together with a majority voting scheme is presented in this paper. Previous research with regards to predict the presence of Parkinson¡¯s Disease has shown accuracy rates up to 92.9% [1] but it comes with a cost of reduced prediction accuracy of the minority class. The designed neural network system boosted by filtering in this article presents a significant increase of robustness and it is shown that by majority voting of the parallel networks, recognition rates reach to > 90 in a imbalanced 3:1 imbalanced class distribution Parkinson¡¯s Disease data set.
%K machine learning
%K parallel neural networks
%K boosting by filtering
%K Parkinson¡¯s disease
%U http://www.scjournal.com.ba/index.php/scjournal/article/view/37