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Prediction of Parkinson’s Disease using Machine Learning Techniques on Speech Dataset


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1 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
     

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In the present decade of accelerated advances in Medical Sciences, most studies fail to lay focus on ageing diseases. These are diseases that display their symptoms at a much advanced stage and makes a complete recovery almost improbable. Parkinson’s disease (PD) is the second most commonly diagnosed neurodegenerative disorder of the brain. One could argue, that it is almost incurable and inflicts a lot of pain on the patients. All these make it quite clear that there is an oncoming need for efficient, dependable and expandable diagnosis of Parkinson’s disease. A dilemma of this intensity requires the automating of the diagnosis to lead accurate and reliable results. It has been observed that most PD Patients demonstrate some sort of impairment in speech or speech dysphonia, which makes speech measurements and indicators one of the most important aspects in prediction of PD. The aim of this work is to compare various machine learning models in the successful prediction of the severity of Parkinson’s disease and develop an effective and accurate model in order to help diagnose the disease accurately at an earlier stage which could in turn help the doctors to assist in the cure and recovery of PD Patients. For the aforementioned purpose we plan on using the Parkinson’s Tele monitoring dataset which was acquired from the UCIML repository.

Keywords

Parkinson’s Disease, PD, Parkinson’s Tele Monitoring, Backpropagation, Severity Prediction.
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  • Das, R. (2010). A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Systems with Applications, 37(2), 1568-1572.

  • Chen, H. L., Huang, C. C., Yu, X. G., Xu, X., Sun, X., Wang, G., and Wang, S. J. (2013). An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach. Expert systems with applications, 40(1), 263-271.

  • Hariharan, M., Polat, K., and Sindhu, R. (2014). A new hybrid intelligent system for accurate detection of Parkinson's disease. Computer methods and programs in biomedicine, 113(3), 904-913.

  • Åström, F., and Koker, R. (2011). A parallel neural network approach to prediction of Parkinson’s Disease. Expert systems with applications, 38(10), 12470-12474.

  • Challa, K. N. R., Pagolu, V. S., Panda, G., and Majhi, B. (2016, October). An improved approach for prediction of Parkinson's disease using machine learning techniques. In Signal Processing, Communication, Power and Embedded System (SCOPES), 2016 International Conference on (pp. 1446-1451). IEEE.

  • Agarwal, A., Chandrayan, S., and Sahu, S. S. (2016, March). Prediction of Parkinson's disease using speech signal with Extreme Learning Machine. In Electrical, Electronics, and Optimization Techniques (ICEEOT), International Conference on (pp. 3776-3779). IEEE.

  • Arora, S., Venkataraman, V., Zhan, A., Donohue, S., Biglan, K. M., Dorsey, E. R., and Little, M. A. (2015). Detecting and monitoring the symptoms of Parkinson's disease using smart phones: A pilot study. Parkinsonism & related disorders, 21(6), 650-653.

  • Asgari, M., and Shafran, I. (2010, August). Predicting severity of Parkinson's disease from speech. In Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE (pp. 5201-5204). IEEE.

  • Sriram, T. V., Rao, M. V., Narayana, G. S., and Kaladhar, D. S. V. G. K. (2016). ParkDiag: A Tool to Predict Parkinson Disease using Data Mining Techniques from Voice Data. International Journal of Engineering Trends and Technology (IJETT), 31(3), 136-140.

  • Kim, J., Nasir, M., Gupta, R., Segbroeck, M. V., Bone, D., Black, M. P., and Narayanan, S. S. (2015). Automatic estimation of Parkinson's disease severity from diverse speech tasks. In Sixteenth Annual Conference of the International Speech Communication Association.

  • Lewis, S. J. G., Foltynie, T., Blackwell, A. D., Robbins, T. W., Owen, A. M., & Barker, R. A. (2005). Heterogeneity of Parkinson’s disease in the early clinical stages using a data driven approach. Journal of Neurology, Neurosurgery and Psychiatry, 76(3), 343-348.

  • Little, M. A., McSharry, P. E., Hunter, E. J., Spielman, J., and Ramig, L. O. (2009). Suitability of dysphonia measurements for telemonitoring of Parkinson's disease. IEEE transactions on biomedical engineering, 56(4), 1015-1022.

  • Martínez-Martín, P., Rodríguez-Blázquez, C., Alvarez, M., Arakaki, T., Arillo, V. C., Chaná, P., and Serrano-Duenas, M. (2015). Parkinson's disease severity levels and MDS-Unified Parkinson's Disease Rating Scale. Parkinsonism and related disorders, 21(1), 50-54.

  • Nilashi, M., Ibrahim, O., and Ahani, A. (2016). Accuracy improvement for predicting Parkinson’s disease progression. Scientific reports, 6, 34181.

  • Tsanas, A., Little, M. A., McSharry, P. E., and Ramig, L. O. (2010). Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests. IEEE transactions on Biomedical Engineering, 57(4), 884-893.

  • Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

  • J Bagyalakshmi, S Vijayaraj, Sindhu, TK Ravi. Method Development and Validation of Erythrosine (E127) Using RP-HPLC Coupled With PDA Detector. Asian J. Research Chem. 1(2): Oct.-Dec. 2008; Page 95-96.

  • Atul H Kategaonkar, Dhaval M Patel, Bhushan P Itkar, Vishnu P Choudhari, Bhanudas S Kuchekar, Ana G Nikalje. Spectrophotometric and Stability Indicating RP-HPLC-PDA method for Simultaneous Determination of Finasteride and Tamsulosin in Combined Tablet Dosage form. Asian J. Research Chem. 2(4):Oct.-Dec. 2009 page 414-419.

  • TentuNageswara Rao, Muralidhar Reddy Avuthu, B. Venkata Reddy, SNVS Murthy. Forced degradation study of TerbinafineHCl by HPLC with PDA detector. Asian J. Research Chem. 2016; 9(11): 561-565.

  • Shishir Shrivastava, Ajay Kumar Akasapu, Lokesh Sharma. Knowledge Discovery in Spatial Databases. Int. J. Tech. 1(1): Jan.-June. 2011; Page 23-28

  • Parth R. Shah, Pratik J. Gupta, Rushant S. Sabnis. Recent Advancementsin Smart Phone Charging. Int. J. Tech. 2017; 7(1): 69-78.

  • K. Shukla, S.S. Shukla, V. Jain, R. Pandey, S. Jain, Swarnlata Saraf, S. Saraf. Fingerprinting of Traditional Medicines through RAPD Technology: - A Newer Approach. Research J. Pharm. and Tech. 1(2): April-June. 2008; Page 63-68.

  • Susanta Kumar Rout, Saptarshi Dutta, Mukul Sengupta, Srimanta Das. Evaluation and Comparison Study of Acetylcholine and Histamine on Isolated Chick, Rat, Guinea pig and Rabbit Ileum with Special Reference to PD2 Value in Pharmacology Practical. Research J. Pharm. and Tech. 4(5): May 2011; Page 715-718.

  • Neveda Baskeran. Iron Deficiency and Brain Disorders. Research J. Pharm. and Tech. 7(3): Mar., 2014; Page 352-353.

  • P. Maheshwari, I. Somasundaram. Health Related Quality of Life Measurement in Asthma and Chronic Obstructive Pulmonary Disease. Research J. Pharm. and Tech. 2016; 9(5): 518-520.

  • Harikumar Rajaguru, Sunil Kumar Prabhakar. Wavelet Neural Networks, Elman Back propagation and Multilayer Perceptrons for Epilepsy Classification from EEG Signals. Research J. Pharm. and Tech 2018; 11(4):1301-1306.

  • Himadri Shekhaar Baul, Muniyan Rajiniraja. Favorable binding of Quercetin to α-Synuclein as potential target in Parkinson disease: An Insilico approach. Research J. Pharm. and Tech. 2018; 11(1): 203-206.


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  • Prediction of Parkinson’s Disease using Machine Learning Techniques on Speech Dataset

Abstract Views: 229  |  PDF Views: 0

Authors

Basil K. Varghese
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
D. Geraldine Bessie Amali
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
K. S. Uma Devi
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

Abstract


In the present decade of accelerated advances in Medical Sciences, most studies fail to lay focus on ageing diseases. These are diseases that display their symptoms at a much advanced stage and makes a complete recovery almost improbable. Parkinson’s disease (PD) is the second most commonly diagnosed neurodegenerative disorder of the brain. One could argue, that it is almost incurable and inflicts a lot of pain on the patients. All these make it quite clear that there is an oncoming need for efficient, dependable and expandable diagnosis of Parkinson’s disease. A dilemma of this intensity requires the automating of the diagnosis to lead accurate and reliable results. It has been observed that most PD Patients demonstrate some sort of impairment in speech or speech dysphonia, which makes speech measurements and indicators one of the most important aspects in prediction of PD. The aim of this work is to compare various machine learning models in the successful prediction of the severity of Parkinson’s disease and develop an effective and accurate model in order to help diagnose the disease accurately at an earlier stage which could in turn help the doctors to assist in the cure and recovery of PD Patients. For the aforementioned purpose we plan on using the Parkinson’s Tele monitoring dataset which was acquired from the UCIML repository.

Keywords


Parkinson’s Disease, PD, Parkinson’s Tele Monitoring, Backpropagation, Severity Prediction.

References