In recent years, neural networks have become very popular in all kinds of prediction problems. In this paper, multiple feed-forward artificial neural networks (ANNs) with various configurations are used in the prediction of Parkinson's disease (PD) of tested individuals, based on extracted features from 26 different voice samples per individual. Results are validated via the leave-one-subject-out (LOSO) scheme. Few feature selection procedures based on Pearson's correlation coefficient, Kendall's correlation coefficient, principal component analysis, and self-organizing maps, have been used for boosting the performance of algorithms and for data reduction. The best test accuracy result has been achieved with Kendall's correlation coefficient-based feature selection, and the most relevant voice samples are recognized. Multiple ANNs have proven to be the best classification technique for diagnosis of PD without usage of the feature selection procedure (on raw data). Finally, a neural network is fine-tuned, and a test accuracy of 86.47% was achieved.
REF used feature selection procedures based on Pearson's and Kendall's correlation coefficients, principal component analysis (PCA), and self-organizing maps (SOM), and multiple feed-forward artificial neural networks (ANNs) for classification, reaching an accuracy of 86.47%.
Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks
Sensors (Basel, Switzerland)
Sensors (Basel, Switzerland)
Medicine Engineering Computer Science