A hybrid intelligent system for the prediction of Parkinson's Disease progression using machine learning techniques

• Autorzy: Nilashi M. , Ibrahim O. , Ahmadi H. , Shahmoradi L. , Farahmand M.

Identyfikatory

DOI

10.1016/j.bbe.2017.09.002

• Języki publikacji: EN

Abstrakty

EN

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-UPDRSand 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.

Słowa kluczowe

EN

healthcare; Parkinson Disease diagnosis; UPDRS; clustering task; dimensionality reduction; ISVR

PL

ochrona zdrowia; choroba Parkinsona; UPDRS; grupowanie; redukcja wymiarowości; ISVR

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 1
• Strony: 1--15
• Opis fizyczny: Bibliogr. 64 poz., rys., tab., wykr.

Twórcy

autor

Nilashi M.

• Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia; Department of Computer Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran

autor

Ibrahim O.

• Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia

autor

Ahmadi H.

• Marine Medicine Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran; Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

autor

Shahmoradi L.

• 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

autor

Farahmand M.

• Department of Computer Science, Abarkouh Branch, Islamic Azad University, Abarkouh, Iran

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Uwagi

PL

W opisie bibliograficznym brak poz. 45-47

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).