Parkinson's Disease Data Classification Using Evolvable Wavelet Neural Networks

摘要

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.