Parkinson’s Disease Prediction using Fisher Score based Recursive Feature Elimination

Abstract

Parkinson’s disease is a neurological syndrome that manifests slowly and gradually, making it difficult to diagnose at an early stage. Voice alterations can be used as a detectable marker of early detection. The Synthetic Minority Oversampling Technique (SMOTE) is employed to address class imbalance issues in the datasets. For optimal feature selection, a novel approach called Fisher Score based Recursive Feature Elimination (FRFE) is proposed, and it is compared with state of art feature selection methods namely Correlation Coefficient, Mutual Information, Backward Feature Elimination, and Recursive Feature Elimination. The performance of models was evaluated across different classifiers using two voice datasets, with different features so as to confirm that FRFE works for any dataset irrespective of features. The FRFE performs better than the state of methods of comparison in terms of accuracy and variance.