Disease vocal diagnosis data 90 times faster.'
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bibliography:
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title: 'Distance Correlation Sure Independence Screening for Accelerated Feature Selection in Parkinson’s Disease Vocal Data '
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[***Keywords —*** feature selection, distance correlation, sure independence screening, Minimum Redundancy-Maximum Relevance, tunable Q-factor wavelet transform, Parkinson’s Disease telemonitoring]{}

Introduction
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Parkinson’s Disease (PD) is a neurodegenerative motion disorder through which speech, voice, and body movements may malfunction. PD is one of the most common diseases after Alzheimer’s spreading progressively over time in a human body [@compare]. Consequently, the disease can be diagnosed by estimating dysphonia produced by a subject who may have PD. Audio data is notoriously high dimensional. So, some forms of feature derivation and selection are required. Preferably, we would use a stable feature selection algorithm: one that selects the same kinds of features even if there is a dramatic change in which data points are available [@Gulgezen]. One such type of feature selection, Minimum Redundancy-Maximum Relevance (mRMR), is fairly commonly used for this task. Sakar et al.[@compare] used this method alongside their tunable-Q wavelets since it provides reliable results regardless of model by estimating maximum joint dependency of features, eliminating the redundant features, and reducing the dimensionality. 