two phases to detect Parkinson’s disease using the features obtained from speech signals using hybrid machine learning methods. Synthetic Minority Over-Sampling Technique (SMOTE) is used to transform imbalance data. Then they use Random Forests classification for classification of Parkinson’s disease datasets to generate promising results.

John Wu [@john] uses a multilayer perceptron artificial network to train and classify PD patients and healthy individuals. They evaluate this algorithm on Parkinson’s disease datasets by emphasizing the imbalanced nature.

Mostafa et al. [@mostafa] evaluated the performances of Decision Tree, Naive Bayes, and Neural Network classification methods for diagnosis of Parkinson’s disease. They conclude that Decision Tree produces the highest accuracy (91.63%), followed by Neural Network (91.01%) and Naive Bayes (89.46%).

Gunduz [@deep] proposes two frameworks based on Convolutional Neural Networks (CNN) to classify Parkinson’s Disease using sets of vocal (speech) features. They use 9-layered CNN as inputs and pass the feature sets to parallel input layers. They extract in-depth features from these parallel branches that are effective in boosting up discriminative power of the classifiers.

Wang et al. [@xgboost] present a python package named Imbalance-XGBoost by combining XGBoost software with weighted and focal losses to deal with binary label-imbalanced classification tasks. They use 