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Diagnosis of Parkinson’s Disease by Boosted Neural NetworksKeywords: machine learning , parallel neural networks , boosting by filtering , Parkinson’s disease Abstract: 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.
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