AUTHOR=Channa Asma , Cramariuc Oana , Memon Madeha , Popescu Nirvana , Mammone Nadia , Ruggeri Giuseppe 

TITLE=Parkinson's disease resting tremor severity classification using machine learning with resampling techniques

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.955464

DOI=10.3389/fnins.2022.955464

ISSN=1662-453X

ABSTRACT=In resting tremor the body part is in complete repose and often dampens or subsides entirely with action. The most frequent cause of resting tremor is known as idiopathic Parkinson's disease (PD). For examining, PD patients’ neurologists include tests such as finger-to-nose test, walking back and forth in the corridor and the pull test. This evaluation is focused on Unified Parkinson’s disease rating scale (UPDRS), that is subjective as well as based on some daily life motor activities for a limited time frame. In this study severity analysis is performed on an imbalanced dataset of PD patients. This is the reason for which the classification of various data containing imbalanced class distribution has endured a notable drawback of the performance achievable by various standard classification learning algorithms. In this work we used resampling techniques including under-sampling, over-sampling and a hybrid combination. Resampling techniques are incorporated with renowned classifiers, such as XGBoost, decision tree and K-nearest neighbors. From the results, it is concluded that Over-sampling method performed much better than under-sampling and hybrid sampling techniques. Among the over-sampling techniques random sampling has obtained 99% accuracy using XGBoost classifier and 98% accuracy using decision tree. Besides, it is observed that different resampling methods performed differently with various classifiers.