Predicting Parkinson's Disease Progression: Evaluation of Ensemble Methods in Machine Learning. (3rd February 2022)

• Record Type: Journal Article
• Authors: Nilashi, Mehrbakhsh; Abumalloh, Rabab Ali; Minaei-Bidgoli, Behrouz; Samad, Sarminah; Yousoof Ismail, Muhammed; Alhargan, Ashwaq; Abdu Zogaan, Waleed
• Other Names: Kumar Reddy M. Praveen Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2022(2022)
• Publication Date: 2022-02-03
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2022/2793361
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : Parkinson's disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS.