Selection of vocal features for Parkinson's Disease diagnosis


Kursun O., Gumus E. , Sertbas A. , Favorov O. V.

INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, vol.6, no.2, pp.144-161, 2012 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 6 Issue: 2
  • Publication Date: 2012
  • Doi Number: 10.1504/ijdmb.2012.048196
  • Title of Journal : INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS
  • Page Numbers: pp.144-161

Abstract

Parkinson's Disease (PD) is a neurodegenerative motor system disorder, which also causes vocal impairments for most of its patients. A number of recent exploratory studies have evaluated the feasibility of detecting voice disorders by applying data mining tools to acoustic features extracted from speech recordings of patients. Selection of a minimal yet descriptive set of features is crucial for improving the classifier generalisation capability and interpretability of the classification model as well as for reducing the burden of data preprocessing. We propose a hybrid of feature selection and cross-validation procedures to lower the bias in the assessment of classifier accuracy.

Parkinson's Disease (PD) is a neurodegenerative motor system disorder, which also causes vocal impairments for most of its patients. A number of recent exploratory studies have evaluated the feasibility of detecting voice disorders by applying data mining tools to acoustic features extracted from speech recordings of patients. Selection of a minimal yet descriptive set of features is crucial for improving the classifier generalisation capability and interpretability of the classification model as well as for reducing the burden of data preprocessing. We propose a hybrid of feature selection and cross-validation procedures to lower the bias in the assessment of classifier accuracy.