A Hybrid Intelligent System for the Prediction of Parkinson's Disease Progression Using Machine Learning Techniques

A Hybrid Intelligent System for the Prediction of Parkinson's Disease Progression Using Machine Learning Techniques Publisher

Nilashi M^{1, 2} ; Ibrahim O¹ ; Ahmadi H^{3, 4} ; Shahmoradi L⁵ ; Farahmand M⁶

Show Affiliations

1. Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310, Johor, Malaysia
2. Department of Computer Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran
3. Marine Medicine Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
4. Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
5. Health Information Management Department, 5th Floor, School of Allied Medical Sciences, Tehran University of Medical Sciences, No #17, Farredanesh Alley, Ghods St, Enghelab Ave, Tehran, Iran
6. Department of Computer Science, Abarkouh Branch, Islamic Azad University, Abarkouh, Iran

Source: Biocybernetics and Biomedical Engineering Published:2018

Abstract

Parkinson's Disease (PD) is a progressive degenerative disease of the nervous system that affects movement control. Unified Parkinson's Disease Rating Scale (UPDRS) is the baseline assessment for PD. UPDRS is the most widely used standardized scale to assess parkinsonism. Discovering the relationship between speech signal properties and UPDRS scores is an important task in PD diagnosis. Supervised machine learning techniques have been extensively used in predicting PD through a set of datasets. However, the most methods developed by supervised methods do not support the incremental updates of data. In addition, the standard supervised techniques cannot be used in an incremental situation for disease prediction and therefore they require to recompute all the training data to build the prediction models. In this paper, we take the advantages of an incremental machine learning technique, Incremental support vector machine, to develop a new method for UPDRS prediction. We use Incremental support vector machine to predict Total-UPDRS and Motor-UPDRS. We also use Non-linear iterative partial least squares for data dimensionality reduction and self-organizing map for clustering task. To evaluate the method, we conduct several experiments with a PD dataset and present the results in comparison with the methods developed in the previous research. The prediction accuracies of method measured by MAE for the Total-UPDRS and Motor-UPDRS were obtained respectively MAE = 0.4656 and MAE = 0.4967. The results of experimental analysis demonstrated that the proposed method is effective in predicting UPDRS. The method has potential to be implemented as an intelligent system for PD prediction in healthcare. © 2017 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences

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Experts (# of related papers)

Leila Shahmoradi (5)

Style	Citing Format
MLA	Nilashi M, et al.. "A Hybrid Intelligent System for the Prediction of Parkinson's Disease Progression Using Machine Learning Techniques." Biocybernetics and Biomedical Engineering, vol. 38, no. 1, 2018, pp. 1-15.
APA	Nilashi M, Ibrahim O, Ahmadi H, Shahmoradi L, Farahmand M (2018). A Hybrid Intelligent System for the Prediction of Parkinson's Disease Progression Using Machine Learning Techniques. Biocybernetics and Biomedical Engineering, 38(1), 1-15.
Chicago	Nilashi M, Ibrahim O, Ahmadi H, Shahmoradi L, Farahmand M. "A Hybrid Intelligent System for the Prediction of Parkinson's Disease Progression Using Machine Learning Techniques." Biocybernetics and Biomedical Engineering 38, no. 1 (2018): 1-15.
Harvard	Nilashi M et al. (2018) 'A Hybrid Intelligent System for the Prediction of Parkinson's Disease Progression Using Machine Learning Techniques', Biocybernetics and Biomedical Engineering, 38(1), pp. 1-15.
Vancouver	Nilashi M, Ibrahim O, Ahmadi H, Shahmoradi L, Farahmand M. A Hybrid Intelligent System for the Prediction of Parkinson's Disease Progression Using Machine Learning Techniques. Biocybernetics and Biomedical Engineering. 2018;38(1):1-15.
BibTex	@article{ author = {Nilashi M and Ibrahim O and Ahmadi H and Shahmoradi L and Farahmand M}, title = {A Hybrid Intelligent System for the Prediction of Parkinson's Disease Progression Using Machine Learning Techniques}, journal = {Biocybernetics and Biomedical Engineering}, volume = {38}, number = {1}, pages = {1-15}, year = {2018} }
RIS	TY - JOUR AU - Nilashi M AU - Ibrahim O AU - Ahmadi H AU - Shahmoradi L AU - Farahmand M TI - A Hybrid Intelligent System for the Prediction of Parkinson's Disease Progression Using Machine Learning Techniques JO - Biocybernetics and Biomedical Engineering VL - 38 IS - 1 SP - 1 EP - 15 PY - 2018 ER -

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