- Title
- Parkinson’s disease data classification using evolvable wavelet neural networks
- Creator
- Khan, Maryam Mahsal; Chalup, Stephan K.; Mendes, Alexandre
- Relation
- Second Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2016). Artificial Life and Computational Intelligence: Second Australasian Conference on Artificial Life and Computational Intelligence (presented in Lecture Notes in Computer Science, Vol. 9592) (Canberra, A.C.T. 2-5 Feburary, 2016) p. 113-124
- Publisher Link
- http://dx.doi.org/10.1007/978-3-319-28270-1_10
- Publisher
- Springer
- Resource Type
- conference paper
- Date
- 2016
- Description
- Parkinson’s Disease is the second most common neurological condition in Australia. This paper develops and compares a new type of Wavelet Neural Network that is evolved via Cartesian Genetic Programming for classifying Parkinson’s Disease data based on speech signals. The classifier is trained using 10-fold and leave-one-subject-out cross validation testing strategies. The results indicate that the proposed algorithm can find high quality solutions and the associated features without requiring a separate feature pruning pre-processing step. The technique aims to become part of a future support tool for specialists in the early diagnosis of the disease reducing misdiagnosis and cost of treatment.
- Subject
- Parkinson’s disease; neuroevolution; wavelet neuralnetwork; Cartesian genetic programming; artificial neural network
- Identifier
- http://hdl.handle.net/1959.13/1324055
- Identifier
- uon:24949
- Identifier
- ISBN:9783319282695
- Rights
- The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-28270-1_10
- Language
- eng
- Full Text
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