also from their predictive ability.

Related works
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DC-SIS is compared directly to that of Sakar et al.[@compare]. In their study, they apply tunable Q-factor wavelet transform (TQWT) to the voice signals of PD patients and generate discriminating features from summary statistics of a bag-of-waves model. They combine several earlier feature sets and compares the performance of TQWT with them. The result of the study suggests the better performance of TQWT in terms of accuracy and complementary information in PD classification to combine feature selection and develop an improved system with an ensemble model.

With Sakar et al. are focused on their wavelets, their ensemble method still rests upon the features that it receives. These features are chosen via the work of Peng et al. [@feature] who investigate how to select excellent features according to maximal statistical dependency criterion based on mutual information. They derive an equivalent form of maximal dependency condition named minimum redundancy-maximum relevance (mRMR) for first-order incremental feature selection. Since mRMR is model agnostic, it can be combined with other feature selectors, which can significantly improve classification accuracy.

Some other studies that work with similar or identical data include, Polat [@smote] who proposes a new method with 