%0 Journal Article
%T Neural Networks to Diagnose the Parkinson¡¯s Disease
%A Mehmet Can
%J Southeast Europe Journal of Soft Computing
%D 2013
%I 
%X To identify the presence of Parkinson¡¯s disease, a neural network system with back propagation together with a majority voting scheme is presented in this paper. The data used has an imparity of the ratio 3:1. Previous research with regards to predict the presence of the disease has shown accuracy rates up to 92.9% [1] but it comes with a cost of reduced prediction accuracy of the small class. The designed neural network system is boosted by filtering, and this causes a significant increase of robustness. It is also shown that by majority voting of eleven parallel networks, recognition rates reached to > 90 in spite of 3:1 imbalanced class distribution of the 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/47