Parkinson’s disease (PD) is a highly common neurological disease
affecting a large population worldwide. Several studies revealed that
the degradation of voice is one of its initial symptoms, which is also
known as dysarthria. In this work, we attempt to explore and harness the
correlation between various features in the voice samples observed in PD
subjects. To do so, a novel two-level ensemble-based feature selection
method has been proposed, whose results were combined with an MLP based
classifier using K-fold cross-validation as the re-sampling strategy.
Three separate benchmark datasets of voice samples were used for the
experimentation work. Results strongly suggest that the proposed feature
selection framework helps in identifying an optimal set of features
which further helps in highly accurate identification of PD patients
using a Multi-Layer Perceptron from their voice samples. The proposed
model achieves an overall accuracy of 98.3%, 95.1% and 100% on the
three selected datasets respectively. These results are significantly
better than those achieved by a non-feature selection based option, and
even the recently proposed chi-square based feature selection option.
A Novel Rolling Bearing Fault Diagnosis Method Based on Adaptive Feature Selection and Clustering