Affiliations: [a] University School of Information, Communication and Technology (USICT), Guru Gobind Singh Indraprastha University (GGSIPU), New Delhi, India | [b] Department of Artificial Intelligence and Data Sciences, Indira Gandhi Delhi Technical University for Women (IGDTUW), Delhi, India
Correspondence:
[*]
Corresponding author: Ritika Kumari, University School of Information, Communication and Technology (USICT), Guru Gobind Singh Indraprastha University (GGSIPU), New Delhi, India. E-mail: [email protected].
Note: [1] An abridged version of this work has been presented at the ICDAM-2023 conference.
Abstract: Parkinson’s disease (PD) is a neurodegenerative condition that affects the neurological, behavioral, and physiological systems of the brain. According to the most recent WHO data, 0.51 percent of all fatalities in India are caused by PD. It is a widely recognized fact that about one million people in the United States suffer from PD, relative to nearly five million people worldwide. Approximately 90% of Parkinson’s patients have speech difficulties. As a result, it is crucial to identify PD early on so that appropriate treatment may be determined. For the early diagnosis of PD, we propose a Bagging-based hybrid (B-HPD) approach in this study. Seven classifiers such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), Naïve Bayes (NB), K nearest neighbor (KNN), Random Under-sampling Boost (RUSBoost) and Support Vector Machine (SVM) are considered as base estimators for Bagging ensemble method and three oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic (ADASYN) and SVMSmote are implemented under this research work. Feature Selection (FS) is also used for data preprocessing and further performance enhancement. We obtain the Parkinson’s Disease classification dataset (imbalanced) from the Kaggle repository. Finally, using two performance measures: Accuracy and Area under the curve (AUC), we compare the performance of the model with ALL features and with selected features. Our study suggests bagging with a base classifier: RF is showing the best performance in all the cases (with ALL features: 754, with FS: 500, with three Oversampling techniques) and may be used for PD diagnosis in the healthcare industry.
