unique XGBoost methods and offer state-of-the-art performances and focus on the new perspective to study the imbalanced datasets.

Cai et al. [@cai] proposed Chaotic Bacterial Foraging Optimization based on the fuzzy k-nearest neighbor (FKNN) method for early PD diagnosis.

Truncer and Dogan [@octopus] have access to the vocal signals themselves, and introduce a novel octopus based feature extraction method to introduce a general signal recognition method. They combined preprocessing, feature extraction, feature selection, classification, and post-processing phases to achieve a high success rate and low computational complexity.

Badem et al. [@badem] have a competing feature selection method based on the artificial Bee Colony metaheuristic to also improve the result with classification methods that we have used in this study.

Methodology
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Data Transformation
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To ensure the statistical guarantees of both the feature selection and the machine learning algorithms, the predictors are standardized before the features are selected. Despite the statistical benefits of standardizing the data, we also applied the analysis using predictor normalization and sample normalization. Neither method of normalization produced results were significantly different from standardization.

Minimum Redundancy-Maximum Relevance
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In Sakar et al.[@compare], the authors use mRMR as a model-agnostic method for feature selection to demonstrate the 