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A comparison of soft computing models for Parkinson’s disease diagnosis using voice and gait features

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Abstract

Parkinson’s disease is a widespread disease among elder population worldwide caused by dopamine loss, which reduces quality of life because of motor and non-motor complications. In the current paper, nine soft computing models, i.e., Cubist, Cubist Committees, Random Forests, Kernel Support Vector Machine, Linear Regression, Naïve Bayes, Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Hybrid Neuro-Fuzzy Inference System are implemented for Parkinson’s disease diagnosis using voice and gait features. Later, their performances are evaluated based on performance measures, viz., true positive, false positive, false negative, true negative, accuracy, sensitivity, specificity, and RMSE, and finally, a comparison is performed to identify the most efficient model and data set combination. The comparison demonstrated that Random Forest model outperformed others yielding 100% accuracy, 100% sensitivity, 100% specificity, and zero RMSE on voice and gait training data sets both; Cubist Committees model outperformed others yielding 74.00% accuracy, 69.39% sensitivity, 78.43% specificity, and 0.4582 RMSE on voice testing data set; Random Forest model once again outperformed others yielding 81.66% accuracy, 92.39% sensitivity, 66.67% specificity, and 0.4283 RMSE on gait testing data set. Furthermore, these models’ performances are also evaluated on reduced feature vector voice and gait data sets obtained by Principal Component Analysis, and compared with their performances on former data sets. The comparison exhibited that the soft computing models’ performance decreases by reducing feature vector of the data sets.

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References

  • Bocklet T, Noth E, Stemmer G, Ruzickova H, Rusz J (2011) Detection of persons with Parkinson’s disease by acoustic, vocal, and prosodic analysis. In: IEEE workshop on automatic speech recognition and understanding

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  MATH  Google Scholar 

  • Caesarendra W, Putri FT, Ariyanto M, Setiawan JD (2015) Pattern recognition methods for multi stage classification of parkinson’s disease utilizing voice features. In: IEEE international conference on advanced intelligent mechatronics, Busan, Korea, pp 802–807

  • Chen SW, Lin SH, Liao LD, Lai HY, Pei YC, Kuo TS et al (2011) Quantification and recognition of Parkinsonian gait from monocular video imaging using kernel-based principal component analysis. Biomed Eng Online 10(1):99. doi:10.1186/1475-925X-10-99 (PMID:22074315)

    Article  Google Scholar 

  • Cho CW, Chao WH, Lin SH, Chen YY (2009) A vision-based analysis system for gait recognition in patients with Parkinson’s disease. Expert Syst Appl 36(3):7033–7039. doi:10.1016/j.eswa.2008.08.076

    Article  Google Scholar 

  • Erdogdu Sakar B, Isenkul M, Sakar CO, Sertbas A, Gurgen F, Delil S, Apaydin H, Kursun O (2013) Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Inf 17(4):828–834

    Article  Google Scholar 

  • Frenkel-Toledo S, Giladi N, Peretz C, Herman T, Gruendlinger L, Hausdorff JM (2005) Treadmill walking as a pacemaker to improve gait rhythm and stability in Parkinson’s disease. Mov Disord 20(9):1109–1114

    Article  Google Scholar 

  • Geyer CJ (2003) Generalized linear models in R. https://pdfs.semanticscholar.org/ba02/e8b16747e56c0202281206220e16d90e9c0f.pdf. Accessed 13 May 2017

  • Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE, PhysioBank, PhysioToolkit, & PhysioNet (2013) Components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220. doi:10.1161/01.CIR.101.23.e215

    Article  Google Scholar 

  • Hausdorff JM, Lowenthal J, Herman T, Gruendlinger L, Peretz C, Giladi N (2007) Rhythmic auditory stimulation modulates gait variability in Parkinson’s disease. Eur J Neurosci 26(8):2369–2375

    Article  Google Scholar 

  • Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  • Ji W, Li Y (2012) Energy-based feature ranking for assessing the dysphonia measurements in Parkinson detection. IET Signal Proc 6(4):300–305

    Article  MathSciNet  Google Scholar 

  • Karatzoglou A, Meyer D, Hornik K (2006) Support vector machines in R. J Stat Softw 15(9):1–28

    Article  Google Scholar 

  • Khan T, Westin J, Dougherty M (2013) Motion cue analysis for Parkinsonian gait recognition. Open Biomed Eng J 7(1):1–8

    Article  Google Scholar 

  • Kim J, Kasabov N (1999) HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems. Neural Networks 12(9):1301–1319

    Article  Google Scholar 

  • Klucken J, Barth J, Kugler P, Schlachetzki J, Henze T, Marxreiter F et al (2013) Unbiased and mobile gait analysis detects motor impairment in Parkinson’s disease. PLoS One 8(2):e56956

    Article  Google Scholar 

  • Koh SB, Park KW, Lee DH, Kim SJ, Yoon JS (2008) Gait analysis in patients with Parkinson’s disease: relationship to clinical features and freezing. J Mov Disord 1(2):59–64. doi:10.14802/jmd.08011

    Article  Google Scholar 

  • Kuhn M, Weston S, Keefer C, Coulter N (2012) Cubist models for regression. https://cran.rproject.org/web/packages/Cubist/vignettes/cubist.pdf. Accessed 01 May 2017

  • Li S, Wang J, Wang X (2010) A novel gait recognition analysis system based on body sensor networks for patients with Parkinson’s disease. In: Proceedings of IEEE Globecom 2010 workshop on advanced sensor integration technology, pp 256–260

  • Lichman M (2013) UCI machine learning repository. University of California, School of Information and Computer Science, Irvine, CA. http://archive.ics.uci.edu/ml. Accessed 22 Mar 2016

  • Mitchell TM (1997) Machine Learning. McGraw-Hill, New York

    MATH  Google Scholar 

  • Parisi F, Ferrari G, Giuberti M, Contin L, Cimolin V et al (2015) Body-sensor-network-based kinematic characterization and comparative outlook of UPDRS scoring in leg agility, sit-to-stand, and gait tasks in Parkinson’s disease. IEEE J Biomed Health Inf 19(6):1777–1793

    Article  Google Scholar 

  • Pun UK, Gu H, Dong Z, Artan S (2016) Classification and visualization tool for gait analysis of Parkinson’s disease. In: 38th annual international conference of the IEEE engineering in medicine and biology society, pp 2407–2410

  • Setty S, Rao YS (2016) SVM based machine learning approach to identify Parkinson’s disease using gait analysis. In: 2016 international conference on inventive computation technologies, Coimbatore, India

  • Salarian A, Russmann H, Vingerhoets FJ, Dehollain C, Blanc Y, Burkhard PR, Aminian K (2004) Gait assessment in Parkinson’s disease: toward an ambulatory system for long-term monitoring. IEEE Trans Bio-Med Eng 51(8):1434–1443. doi:10.1109/TBME.2004.827933 (PMID:15311830)

    Article  Google Scholar 

  • Shirvan RA, Tahami E (2011) Voice analysis for detecting Parkinson’s disease using genetic algorithm and KNN classification method. In: 18th Iranian conference on biomedical engineering

  • Shukla A, Rathore CP, Bhansali N (2016) Parkinson’s disease detection with gait recognition using soft computing techniques. Optimizing assistive technologies for aging populations. IGI Global, Hershey, pp 357–377

    Book  Google Scholar 

  • Su M, Chuang KS (2015) Dynamic feature selection for detecting Parkinson’s disease through voice signal. In: IEEE MTT-S 2015 international microwave workshop series on RF and wireless technologies for biomedical and healthcare applications, pp 148–149

  • Tsanas A, Little MA, McSharry PE, Ramig LO (2010) Accurate telemonitoring of Parkinson’s disease progression by noninvasive speech tests. IEEE Trans Biomed Eng 57(4):884–893. doi:10.1109/TBME.2009.2036000

    Article  Google Scholar 

  • Wahid F, Begg RK, Hass CJ, Halgamuge S, Ackland DC (2015) Classification of Parkinson’s disease gait using spatial-temporal gait features. IEEE J Biomed Health Inf 19(6):1794–1802

    Article  Google Scholar 

  • Wibawa MS, Nugroho HA, Setiawan NA (2015) Performance evaluation of combined feature selection and classification methods in diagnosing parkinson disease based on voice feature. In: International conference on science in information technology, Yogyakarta, Indonesia, pp 126–131

  • Yadav G, Kumar Y, Sahoo G (2011) Predication of Parkinson’s disease using data mining methods: a comparative analysis of tree, statistical and support vector machine classifiers. Indian J Med Sci 65(6):231–242. doi:10.4103/0019-5359.107023

    Article  Google Scholar 

  • Yogev G, Giladi N, Peretz C, Springer S, Simon ES, Hausdorff JM (2005) Dual tasking, gait rhythmicity, and Parkinson’s disease: which aspects of gait are attention demanding? Eur J Neurosci 22:1248–1256

    Article  Google Scholar 

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Authors and Affiliations

  1. National Institute of Technology, Raipur, Chhattisgarh, India

    Rekh Ram Janghel

  2. Dr. C. V. Raman University, Kota, Bilaspur, Chhattisgarh, India

    Chandra Prakash Rathore

  3. ABV-Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh, India

    Anupam Shukla

  4. Chhattisgarh Swami Vivekananda Technical University, Bhilai, Chhattisgarh, India

    Kshitiz Verma & Swati Rathore

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  1. Rekh Ram Janghel
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  2. Anupam Shukla
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  3. Chandra Prakash Rathore
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  4. Kshitiz Verma
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  5. Swati Rathore
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Correspondence to Rekh Ram Janghel.

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Janghel, R., Shukla, A., Rathore, C. et al. A comparison of soft computing models for Parkinson’s disease diagnosis using voice and gait features. Netw Model Anal Health Inform Bioinforma 6, 14 (2017). https://doi.org/10.1007/s13721-017-0155-8

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  • DOI: https://doi.org/10.1007/s13721-017-0155-8

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