1,289
Views
38
CrossRef citations to date
0
Altmetric
Review Article

A Comparative Study of Existing Machine Learning Approaches for Parkinson's Disease Detection

&

References

  • J. Parkinson, An Essay on Shaking Palsy. London: Whittingham and Rowland Printing, 1817.
  • D. B. Calne, “Is idiopathic parkinsonism the consequence of an event or a process,” Neurology, Vol. 44, no. 15, pp. 5–5, 1994. doi: 10.1212/WNL.44.1.5
  • A. E. Lang and A. M. Lozano, “Parkinson’s disease first of Two parts,” New England J. Med., Vol. 339, pp. 1044–53, 1998. doi: 10.1056/NEJM199810083391506
  • A Samii, J. G. Nutt, and B. R. Ransom, “Parkinson’s disease,” Lancet, Vol. 363, no. 9423, pp. 1783–93, 2004. doi: 10.1016/S0140-6736(04)16305-8
  • L. M. de Lau and M. M. Breteler, “Epidemiology of Parkinson’s disease,” Lancet Neurol., Vol. 5, pp. 525–35, 2006. doi: 10.1016/S1474-4422(06)70471-9
  • E. M. Morris, “Movement disorder in people with Parkinson disease: A model for physical therapy,” Phys. Ther., Vol. 80, pp. 578–97, 2000.
  • A. Schrag, C. D. Good, K. Miszkiel, H. R. Morris, C. J. Mathias, A. J. Lees, and N. P. Quinn, “Differentiation of atypical parkinsonian syndromes with routine MRI,” Neurology, Vol. 54, pp. 697–702, 2000. doi: 10.1212/WNL.54.3.697
  • R. Angel, W. Alston, and J. R. Higgins, “Control of movement in Parkinson’s disease,” Brain, Vol. 93, no. 1, pp. 1–14, 1970. doi: 10.1093/brain/93.1.1
  • S. L. Wu, R. M. Liscic, S. Kim, S. Sorbi, and Y. H. Yang, “Nonmotor symptoms of Parkinson’s disease,” Parkinson’s Dis., 2017. DOI:10.1155/2017/4382518.
  • T. Yousaf, H. Wilson, and M. Politis, “Imaging the nonmotor symptoms in Parkinson’s disease,” Int. Rev. Neurobiol., Vol. 133, pp. 179–257, 2017. doi: 10.1016/bs.irn.2017.05.001
  • C. G. Goetz, et al., “Movement disorder society-sponsored revision of the unified Parkinson’s disease rating scale (MDS-UPDRS): Scale presentation and clinimetric testing results,” Mov. Disord., Vol. 23, no. 15, pp. 2129–70, 2008. doi: 10.1002/mds.22340
  • B. Post, M. P. Merkus, R. M. de Bie, R. J. de Haan, and J. D. Speelman, “Unified Parkinson’s disease rating scale motor examination: Are ratings of nurses, residents in neurology, and movement disorders specialists interchangeable?,” Movement Dis., Vol. 20, pp. 1577–84, 2005. doi: 10.1002/mds.20640
  • R. Das, “A comparison of multiple classification methods for diagnosis of Parkinson disease,” Expert Syst. Appl., Vol. 37, pp. 1568–72, 2010. doi: 10.1016/j.eswa.2009.06.040
  • F. Astrom and R. Koker, “A parallel neural network approach to prediction of Parkinson’s disease,” Expert Syst. Appl., Vol. 38, pp. 12470–4, 2011. doi: 10.1016/j.eswa.2011.04.028
  • S. Pan, S. Iplikci, K. Warwick, and T. Z. Aziz, “Parkinson’s disease tremor classification – a comparison between support vector machines and neural networks,” Expert Syst. Appl., Vol. 39, pp. 10764–71, 2012. doi: 10.1016/j.eswa.2012.02.189
  • O. Eskidere, F. Ertas, and C. Hanilci, “A comparison of regression methods for remote tracking of Parkinson’s disease progression,” Expert Syst. Appl., Vol. 39, pp. 5523–8, 2012. doi: 10.1016/j.eswa.2011.11.067
  • S.-H. Lee and J. S. Lim, “Parkinson’s disease classification using gait characteristics and wavelet-based feature extraction,” Expert Syst. Appl., Vol. 39, pp. 7388–44, 2012.
  • G. Sateesh Babu, and S. Suresh, “Parkinson’s disease prediction using gene expression – a projection based learning meta-cognitive neural classifier approach,” Expert Syst. Appl., Vol. 40, pp. 1519–29, 2013. doi: 10.1016/j.eswa.2012.08.070
  • M. Hariharan, K. Polat, and R. Sindhu, “A new hybrid intelligent systems for accurate detection of Parkinson’s disease,” Comp. Methods Prog. Biomed., Vol. 113, pp. 904–13, 2014. doi: 10.1016/j.cmpb.2014.01.004
  • W. Zeng and C. Wang, “Classification of neurodegenerative diseases using gait dynamics via deterministic learning,” Inform. Sci., Vol. 317, pp. 246–58, 2015. doi: 10.1016/j.ins.2015.04.047
  • L. Naranjo, C. J. Perez, J. Martín, and Y. Campos-Roca, “A two-stage variable selection and classification approach for Parkinson’s disease detection by using voice recording replications,” Comp. Methods Prog. Biomed., Vol. 142, pp. 147–56, 2017. doi: 10.1016/j.cmpb.2017.02.019
  • M. A Little, P. E. McSharry, E. J. Hunter, J. Spielman, and L. O. Ramig, “Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease,” IEEE Trans. Biomed. Eng., Vol. 56, pp. 1015–22, 2009. doi: 10.1109/TBME.2008.2005954
  • H.-L. Chen, C-C. Huang, X-G. Yu, X. Xu, X. Sun, G. Wang, and S-J. Wang, “An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach,” Expert Syst. Appl., Vol. 40, pp. 263–71, 2013. doi: 10.1016/j.eswa.2012.07.014
  • Y.-Y. Chen, et al., “A vision-based regression model to evaluate parkinsonian gait from monocular image sequences,” Expert Syst. Appl., Vol. 39, pp. 520–6, 2012. doi: 10.1016/j.eswa.2011.07.042
  • P. Piccini, and A. Whone, “Functional brain imaging in the differential diagnosis of Parkinson's disease,” Lancet Neurol., Vol. 3, pp. 284–90, 2004. doi: 10.1016/S1474-4422(04)00736-7
  • A. G. Filler, “The history, development and impact of computed imaging in neurological diagnosis and neurosurgery: CT, MRI, and DTI,” Nature, Vol. 7, pp. 1–69, 2009.
  • B. S. Mahanand, S. Suresh, N. Sundararajan, and M. Aswatha Kumar, “Identification of brain regions responsible for Alzheimer’s disease using a self-adaptive resource allocation network,” Neural Netw., Vol. 32, pp. 313–22, 2012. doi: 10.1016/j.neunet.2012.02.035
  • A. Schrage, M. Jahanshahi, and N. Quinn, “How does Parkinson’s disease affect quality of life? A comparison with quality of life in the general population,” Movement Disord., Vol. 15, pp. 1112–8, 2000. doi: 10.1002/1531-8257(200011)15:6<1112::AID-MDS1008>3.0.CO;2-A
  • B. Ravina, et al., “The role of radiotracer imaging in Parkinson disease,” Neurology, Vol. 64, pp. 208–15, 2005. doi: 10.1212/01.WNL.0000149403.14458.7F
  • T. Wus, L. Wang, Y. Chen, C. Zhao, K. Li, and P. Chan, “Changes of functional connectivity of the motor network in the resting state in Parkinson’s disease,” Neurosci. Lett., Vol. 460, pp. 6–10, 2009. doi: 10.1016/j.neulet.2009.05.046
  • D. Wu, K. Warwick, Z. Ma, J. G. Burgess, S. Pan, and T. Z. Aziz, “Prediction of Parkinson’s disease tremor onset using radial basis function neural networks,” Expert Syst. Appl., Vol. 37, pp. 2923-2928, 2010. doi: 10.1016/j.eswa.2009.09.045
  • C. Salvatore, et al., “Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and progressive supranuclear palsy,” J. Neurosci. Methods, Vol. 222, pp. 230–37, 2014. doi: 10.1016/j.jneumeth.2013.11.016
  • G. Sateesh Babu, S. Suresh, and B. S. Mahanand, “A novel PBL-McRBFN-RFE approach for identification of critical brain regions responsible for Parkinson’s disease,” Expert Syst. Appl., Vol. 41, pp. 478–88, 2014. doi: 10.1016/j.eswa.2013.07.073
  • B. Rana, A. Juneja, M. Saxena, S. Gudwani, S. Senthil Kumaran, R.K. Agrawal, and M. Behari, “Regions-of-interest based automated diagnosis of Parkinson’s disease using T1-weighted MRI,” Expert Syst. Appl., Vol. 42, pp. 4506–16, 2015. doi: 10.1016/j.eswa.2015.01.062
  • R. Armananzas, C. Bielza, K. R. Chaudhuri, P. Martinez-Martin, and P. Larrañaga, “ Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach,” Artif. Intell. Med., Vol. 58, pp. 195–202, 2013. doi: 10.1016/j.artmed.2013.04.002
  • F. J. Martinez-Murcia, J. M. Górriz, J. Ramírez, I. A. Illán, A. Ortiz, and the PPMI, “Automatic detection of parkinsonism using significance measures and component analysis in DaTSCAN imaging,” Neurocomputing, Vol. 126, pp. 58–70, 2014. doi: 10.1016/j.neucom.2013.01.054
  • G. Singh, and L. Samavedham, “Unsupervised learning based feature extraction for differential diagnosis of neurodegenerative diseases: a case study on early-stage diagnosis of Parkinson disease,” J. Neurosci.Methods, Vol. 256, pp. 30–40, 2015. doi: 10.1016/j.jneumeth.2015.08.011
  • A. Benba, A. Jilbab, and A. Hammouch, “Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA,” Int J. Speech Technol., Vol. 19, no. 4, pp. 743–54, 2016. doi: 10.1007/s10772-016-9367-z
  • M. Dash, and H. Liu, “Feature selection for classification,” Intell. Data Anal., Vol. 1, pp. 131–56, 1997. doi: 10.3233/IDA-1997-1302
  • L. Huan and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining. Boston, MA: Kluwer Academic, 1998.
  • M. E. ElAlami, “A filter model for feature subset selection based on genetic algorithm,” Knowledge Based Syst., Vol. 22, pp. 356–62, 2009. doi: 10.1016/j.knosys.2009.02.006
  • H. Yoon, C-S. Park, J. S. Kim, and J-G. Baek, “Algorithm learning based neural network integrating feature selection and classification,” Expert Syst. Appl., Vol. 40, pp. 231–41, 2013. doi: 10.1016/j.eswa.2012.07.018
  • R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artif. Intell., Vol. 97, pp. 273–324, 1997. doi: 10.1016/S0004-3702(97)00043-X
  • M. Hall, “Correlation based feature selection for machine learning,” Ph.D. dissertation, Dept. of Computer Science, University of Waikato, 1999.
  • A. H. Hadjahmadi and T. J. Askari, “A decision support system for Parkinson’s disease diagnosis using classification and regression tree,” J. Math. Comp. Sci., Vol. 4, pp. 257–63, 2012. doi: 10.22436/jmcs.04.02.15
  • O. Uncu and I. B. Turksen, “A novel feature selection approach: combining feature wrappers and filters,” Inf. Sci., Vol. 177, pp. 449–66, 2007. doi: 10.1016/j.ins.2006.03.022
  • J. Huang, Y. Cai, and X. Xu, “A hybrid genetic algorithm for feature selection wrapper based on mutual information,” Pattern Recog. Lett., Vol. 28, pp. 1825–1844, 2007. doi: 10.1016/j.patrec.2007.05.011
  • D. Guan, W. Yuan, Y-K. Lee, K. Najeebullah, and M. K. Rasel, “A review of ensemble learning based feature selection,” IETE Tech. Rev., Vol. 31, pp. 190–198, 2014. doi: 10.1080/02564602.2014.906859
  • P. Shrivastava, A. Shukla, P. Vepakomma, N. Bhansali, and K. Verma, “A survey of nature-inspired algorithms for feature selection to identify Parkinson’s disease,” Comp. Methods Prog. Biomed., Vol. 139, pp. 171–9, 2017. doi: 10.1016/j.cmpb.2016.07.029
  • M. Poletti, M. Emre, and U. Bonuccelli, “Mild cognitive impairment and cognitive reserve in Parkinson’s disease,” Parkinsonism Related Disord., Vol. 17, pp. 579–586, 2011. doi: 10.1016/j.parkreldis.2011.03.013
  • K Chandrasekaran, S. P. Simon, and N. P. Padhy, “Cuckoo search algorithm for emission reliable economic multi-objective dispatch problem,” IETE J. Res., Vol. 60, pp. 128–38, 2014. doi: 10.1080/03772063.2014.901592
  • V. Mangat and R. Vig, “Dynamic PSO-based associative classifier for medical datasets,” IETE Tech. Rev., Vol. 31, pp. 258–65, 2014. doi: 10.1080/02564602.2014.942237
  • M. A. Little, P. E. McSharry, S. J. Roberts, D. A. Costello, and I. M. Moroz, “Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection,” BioMed. Eng. Online, Vol. 6, p. 23, 2007. doi: 10.1186/1475-925X-6-23
  • A. Khemphila and V. Boonjing, “Parkinsons disease classification using neural network and feature selection,” World Acad. Sci. Tech, Vol. 64, pp. 1–15, 2012.
  • M. T. Hagan and M. Menhaj, “Training feedforward networks with the Marquardt algorithm,” IEEE Trans. Neural Netw., Vol. 5, pp. 989–93, 1994. doi: 10.1109/72.329697
  • V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
  • P. Hall, U. B. Park, and R. J. Samworth, “Choice of neighbor order in nearest-neighbor classification,” Annals Stat., Vol. 36, pp. 2135–52, 2008. doi: 10.1214/07-AOS537
  • I. Scholl, T. Aach, M. T. Deserno, and T. Kuhlen, “Challenges of medical image processing,” Comput Sci Res. Develop., Vol. 26, pp. 5–13, 2011. doi: 10.1007/s00450-010-0146-9

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.