Login Register Cart 0
Generic placeholder image

Current Aging Science

Editor-in-Chief

ISSN (Print): 1874-6098
ISSN (Online): 1874-6128

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Fuzzy Classification Methods Based Diagnosis of Parkinson’s disease from Speech Test Cases

Author(s): Niousha Karimi Dastjerd, Onur Can Sert, Tansel Ozyer and Reda Alhajj*

Volume 12, Issue 2, 2019

Page: [100 - 120] Pages: 21

DOI: 10.2174/1874609812666190625140311

Abstract

Background: Together with the Alzheimer’s disease, Parkinson’s disease is considered as one of the two serious known neurodegenerative diseases. Physicians find it hard to predict whether a given patient has already developed or is expected to develop the Parkinson’s disease in the future. To overcome this difficulty, it is possible to develop a computing model, which analyzes the data related to a given patient and predicts with acceptable accuracy when he/she is anticipated to develop the Parkinson’s disease.

Objectives: This paper contributes an attractive prediction framework based on some machine learning approaches for distinguishing people with Parkinsonism from healthy individuals.

Methods: Several fuzzy classifiers such as Inductive Fuzzy Classifier, Fuzzy Rough Classifier and two types of neuro-fuzzy classifiers have been employed.

Results: The fuzzy classifiers utilized in this study have been tested using the “Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set” of 40 subjects available on the UCI repository.

Conclusion: The results achieved show that FURIA, MLP- Bagging - SGD, genfis2 and scg1 performed the best among the fuzzy rough, WEKA, adaptive neuro-fuzzy and neuro-fuzzy classifiers, respectively. The worst performance belongs to nearest neighborhood, IBK, genfis3 and scg3 among the formerly mentioned classifiers. The results reported in this paper are better in comparison to the results reported in Sakar et al., where the same dataset was used, with utilization of different classifiers. This demonstrates the applicability and effectiveness of the fuzzy classifiers used in this study as compared to the non-fuzzy classifiers used by Sakar et al.

Keywords: Parkinson's disease, data mining, machine learning, fuzzy classification, neuro fuzzy classification, adaptive neuro fuzzy classification.

Next »
Graphical Abstract

[1]
Sakar BE, Isenkul ME, Sakar CO, et al. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Inform 2013; 17(4): 828-34.
[http://dx.doi.org/10.1109/JBHI.2013.2245674] [PMID: 25055311]
[2]
de Lau LM, Breteler MM. Epidemiology of Parkinson’s disease. Lancet Neurol 2006; 5(6): 525-35.
[http://dx.doi.org/10.1016/S1474-4422(06)70471-9] [PMID: 16713924]
[3]
Shahsavari MK, Rashidi H, Bakhsh HR. Efficient classification of Parkinson's disease using extreme learning machine and hybrid particle swarm optimization. 2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA); 2016 June 2; Qazvin, Iran.
[http://dx.doi.org/10.1109/ICCIAutom.2016.7483152]
[4]
Parkinson J. An essay on the shaking palsy. 1817. J Neuropsychiatry Clin Neurosci 2002; 14(2): 223-36.
[http://dx.doi.org/10.1176/jnp.14.2.223] [PMID: 11983801]
[5]
Singh N, Pillay V, Choonara YE. Advances in the treatment of Parkinson’s disease. Prog Neurobiol 2007; 81(1): 29-44.
[http://dx.doi.org/10.1016/j.pneurobio.2006.11.009] [PMID: 17258379]
[6]
Little MA, McSharry PE, Hunter EJ, Spielman J, Ramig LO. Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans Biomed Eng 2009; 56(4): 1015-22.
[http://dx.doi.org/10.1109/TBME.2008.2005954] [PMID: 21399744]
[7]
National Collaborating Centre for Chronic Conditions (UK). Parkinson's Disease: National Clinical Guideline for Diagnosis and Management in Primary and Secondary Care. London: Royal College of Physicians, UK, (2006). (NICE Clinical Guidelines No 35). 5, Diagnosing Parkinson's disease.
[8]
Cunningham L, Mason S, Nugent C, Moore G, Finlay D, Craig D. Home-based monitoring and assessment of Parkinson’s disease. IEEE Trans Inf Technol Biomed 2011; 15(1): 47-53.
[http://dx.doi.org/10.1109/TITB.2010.2091142] [PMID: 21062684]
[9]
Rigas G, Tzallas AT, Tsipouras MG, et al. Assessment of tremor activity in the Parkinson’s disease using a set of wearable sensors. IEEE Trans Inf Technol Biomed 2012; 16(3): 478-87.
[http://dx.doi.org/10.1109/TITB.2011.2182616] [PMID: 22231198]
[10]
Marino S, Ciurleo R, Di Lorenzo G, et al. Magnetic resonance imaging markers for early diagnosis of Parkinson’s disease. In: Neural Regen Res. 2012; 7: p. (8)611.
[11]
Dastgheib ZA, Lithgow B, Moussavi Z. Diagnosis of Parkinson’s disease using electrovestibulography. Med Biol Eng Comput 2012; 50(5): 483-91.
[http://dx.doi.org/10.1007/s11517-012-0890-z] [PMID: 22399163]
[12]
Jeon H, Lee W, Park H, et al. Automatic classification of tremor severity in Parkinson’s disease using a wearable device. Sensors (Basel) 2017; 17(9)E2067
[http://dx.doi.org/10.3390/s17092067]
[13]
Tsanas A, Little MA, McSharry PE, Spielman J, Ramig LO. Novel speech signal processing algorithms for high-accuracy classification of Parkinson’s disease. IEEE Trans Biomed Eng 2012; 59(5): 1264-71.
[http://dx.doi.org/10.1109/TBME.2012.2183367] [PMID: 22249592]
[14]
Tsanas A, Little MA, McSharry PE, Ramig LO. Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity. J R Soc Interface 2011; 8(59): 842-55.
[http://dx.doi.org/10.1098/rsif.2010.0456] [PMID: 21084338]
[15]
Chakraborty A, Chakraborty A, Mukherjee B. Detection of Parkinson’s disease using fuzzy inference system intelligent systems technologies and applications. Springer 2016; pp. 79-90.
[http://dx.doi.org/10.1007/978-3-319-23036-8_7]
[16]
Tsanas A, Little MA, McSharry PE, Ramig LO. Accurate telemonitoring of Parkinson’s disease progression by noninvasive speech tests. IEEE Trans Biomed Eng 2010; 57(4): 884-93.
[http://dx.doi.org/10.1109/TBME.2009.2036000] [PMID: 19932995]
[17]
Samà A, Pérez-López C, Rodríguez-Martín D, et al. Estimating bradykinesia severity in Parkinson’s disease by analysing gait through a waist-worn sensor. Comput Biol Med 2017; 84: 114-23.
[http://dx.doi.org/10.1016/j.compbiomed.2017.03.020] [PMID: 28351715]
[18]
Parisi F, Ferrari G, Giuberti M, et al. Inertial BSN-based characterization and automatic UPDRS evaluation of the gait task of parkinsonians. IEEE Trans Affect Comput 2016; 7: 258-71.
[http://dx.doi.org/10.1109/TAFFC.2016.2549533]
[19]
Murdoch B, Whitehill T, De Letter M, Jones H. Communication impairments in parkinson’s disease. Parkinsons Dis 2011; 2011.
[20]
Hlavica J, Prauzek M, Peterek T, Musilek P. Assessment of Parkinson’s disease progression using neural network and ANFIS models. Neural Netw World 2016; 26: 2.
[http://dx.doi.org/10.14311/NNW.2016.26.006]
[21]
Gürüler H. A novel diagnosis system for Parkinson’s disease using complex-valued artificial neural network with k-means clustering feature weighting method. Neural Comput Appl 2015; 1-10.
[22]
Martinez-Manzanera O, Roosma E, Beudel M, Borgemeester RW, van Laar T, Maurits NM. A method for automatic and objective scoring of bradykinesia using orientation sensors and classification algorithms. IEEE Trans Biomed Eng 2016; 63(5): 1016-24.
[http://dx.doi.org/10.1109/TBME.2015.2480242] [PMID: 26394414]
[23]
Mousavi SJ, Ponnambalam K, Karray F. Inferring operating rules for reservoir operations using fuzzy regression and ANFIS. Fuzzy Sets Syst 2007; 158(10): 1064-82.
[http://dx.doi.org/10.1016/j.fss.2006.10.024]
[24]
Fox CM, Morrison CE, Ramig LO, Sapir S. Current perspectives on the Lee Silverman Voice Treatment (LSVT) for individuals with idiopathic Parkinson disease. Am J Speech Lang Pathol 2002; 11: 2.
[http://dx.doi.org/10.1044/1058-0360(2002/012)]
[25]
Chen HL, Huang CC, Yu XG, et al. An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach. Expert Syst Appl 2013; 40(1): 263-71.
[http://dx.doi.org/10.1016/j.eswa.2012.07.014]
[26]
Mamdani Ebrahim H. Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the institution of electrical engineers. Vol. 121 No. 12, IET, 1974.
[http://dx.doi.org/10.1049/piee.1974.0328]
[27]
El A, Edmonds J, Gonzalez J, Papa M. A framework for hybrid fuzzy logic intrusion detection systems. Proc of IEEE International Conference on Fuzzy Systems. (2005).
[28]
Mitra S, Hayashi Y. Neuro-fuzzy rule generation: Survey in soft computing framework. IEEE Trans Neural Netw 2000; 11(3): 748-68.
[http://dx.doi.org/10.1109/72.846746] [PMID: 18249802]
[29]
Hamdan H, Garibaldi JM. Adaptive neuro-fuzzy inference system (ANFIS) in modelling breast cancer survival. Proc of IEEE International Conference on Fuzzy Systems. 2010
[http://dx.doi.org/10.1109/FUZZY.2010.5583997]
[30]
Choi H, Yoo H, Jung H, Lim T, Lee K, Ahn K. An ANFIS-based energy management inference algorithm with scheduling technique for legacy device. international conference on artificial intelligence, Energy and Manufacturing Engineering (ICAEME'2015) Dubai, 2015.
[31]
Sun CT, Jang JS. A neuro-fuzzy classifier and its applications. Proceedings of IEEE International Conference on Fuzzy Systems. 1993 March 28- April 1; San Francisco, CA, USA.
[http://dx.doi.org/10.1109/FUZZY.1993.327457]
[32]
Cetişli B, Barkana A. Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training. Soft Comput 2010; 14(4): 365-78.
[http://dx.doi.org/10.1007/s00500-009-0410-8]
[33]
Kaufmann MA. Inductive fuzzy classification in marketing analytics. Bern: Ruf (2012) Kaufmann, M A Inductive fuzzy classification in marketing analytics. Switzerland: University of Fribourg 2012.
[34]
Graf C. Erweiterung des Data-Mining-Softwarepakets WEKA um induktive unscharfe Klassifikation 2010.
[35]
Pawlak Z. Rough sets: Theoretical aspects of reasoning about data. Springer Science & Business Media 2012.
[36]
Zadeh LA. Fuzzy sets. Inf Control 1965; 8(3): 338-53.
[http://dx.doi.org/10.1016/S0019-9958(65)90241-X]

Rights & Permissions Print Cite
Article Metrics
20
2
© 2024 Bentham Science Publishers | Privacy Policy