Accuracy Improvement for Predicting Parkinson's Disease Progression.
Parkinson's disease (PD) is a member of a larger group of neuromotor diseases marked by the progressive death of dopamineproducing cells in the brain. Providing computational tools for Parkinson disease using a set of data that contains medical information is very desirable for alleviating the symptoms that can help the amount of people who want to discover the risk of disease at an early stage. This paper proposes a new hybrid intelligent system for the prediction of PD progression using noise removal, clustering and prediction methods. Principal Component Analysis (PCA) and Expectation Maximization (EM) are respectively employed to address the multi-collinearity problems in the experimental datasets and clustering the data. We then apply Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for prediction of PD progression. Experimental results on public Parkinson's datasets show that the proposed method remarkably improves the accuracy of prediction of PD progression. The hybrid intelligent system can assist medical practitioners in the healthcare practice for early detection of Parkinson disease.
Department of Computer Science and Information Systems, Faculty of Computing, Johor, 81310 Skudai, Malaysia.|Department of Computer, Lahijan Branch, Islamic Azad University, Lahijan, Iran.
Mehrbakhsh
M
Nilashi
Department of Computer Science and Information Systems, Faculty of Computing, Johor, 81310 Skudai, Malaysia.|Department of Computer Science and Information Systems, Faculty of Computing, Johor, 81310 Skudai, Malaysia.
Othman
O
Ibrahim
Department of Computer Science and Information Systems, Faculty of Computing, Johor, 81310 Skudai, Malaysia.|Department of Computer Science and Information Systems, Faculty of Computing, Johor, 81310 Skudai, Malaysia.
Ali
A
Ahani
Neurobiol Aging. 2015 Apr;36(4):1765.e1-1765.e6
25623333
J Neurol Sci. 2012 Aug 15;319(1-2):102-4
22656184
Front Aging Neurosci. 2015 Oct 27;7:203
26578949
Neuroimmunomodulation. 2013;20(1):19-28
23154271
IEEE Trans Biomed Eng. 2010 Apr;57(4):884-93
19932995
Am J Epidemiol. 2003 Jun 1;157(11):1015-22
12777365
Artif Intell Med. 2013 Jul;58(3):195-202
23711400
Behav Neurol. 1999 Jan 1;11(3):131-7
22387592
Comput Methods Programs Biomed. 2014 Mar;113(3):904-13
24485390
Aging Dis. 2015 Nov 17;6(6):426-36
26618044
Comput Methods Programs Biomed. 2012 Jul;107(1):36-44
22502984
Nat Genet. 2014 Sep;46(9):989-93
25064009
Parkinsonism Relat Disord. 2009 Dec;15 Suppl 3:S105-9
20082967
PLoS One. 2011 Apr 27;6(4):e19333
21556377
J Aging Phys Act. 2015 Apr;23(2):187-93
24700259
Prog Neurobiol. 2012 Apr;97(1):1-13
22387368
Biotechnol Biotechnol Equip. 2014 Nov 14;28(sup1):S44-S48
26019610
Artif Intell Med. 2011 May;52(1):45-52
21493051
Mol Neurobiol. 2016 Nov;53(9):5876-5892
26497037
Sci Rep. 2016 May 17;6:26145
27184740
Mov Disord. 2012 Jul;27(8):996-1003
22733427
Front Cell Neurosci. 2015 Jul 06;9:247
26217177
J Med Syst. 2012 Aug;36(4):2141-7
21547504
Multivariate Behav Res. 1966 Apr 1;1(2):245-76
26828106
Int J Med Inform. 2013 May;82(5):359-77
23182747