Research Cell: An International Journal of Engineering Sciences

Open Access Open Access  Restricted Access Subscription Access

Causes, Diagnosis And Prediction of Parkinson Diesease : A Review


Affiliations
1 UIET, Panjab University, Chandigarh, India
 

In this review paper an introduction to Parkinson disease, stages in Parkinson disease, causes and the types of Parkinson disease has been discussed. Wearable devices or sensors that can be used for detection of Parkinson disease, checking the accuracy of the results or data provided by the wearable/portable sensors with clinical recorded data has also been discussed. Classifiers that can be used in machine learning for the detection of Parkinson disease have also been discussed. Methods by which Parkinson disease can be detected by medical lab tests have also been discussed. Also, we have discussed the prediction model of Parkinson disease using CNN in machine learning. All results are finally tabulated for comparison.

Keywords

Parkinson Disease, Wearable Devices, Machine Learning, Deep Learning, Machine Learning Classifiers.
User
Notifications
Font Size

  • N. Niemann and J. Jankovic, “Juvenile parkinsonism: Differential diagnosis, genetics, and treatment,” Parkinsonism and Related Disorders, vol. 67. Elsevier Ltd, pp. 74–89, Oct. 01, 2019. doi: 10.1016/j.parkreldis.2019.06.025.

  • P. Rizek, N. Kumar, and M. S. Jog, “An update on the diagnosis and treatment of Parkinson disease,” CMAJ, vol. 188, no. 16. Canadian Medical Association, pp. 1157–1165, Nov. 01, 2016. doi: 10.1503/cmaj.151179.

  • B. Post, L. van den Heuvel, T. van Prooije, X. van Ruissen, B. van de Warrenburg, and J. Nonnekes, “Young Onset Parkinson’s Disease: A Modern and Tailored Approach,” Journal of Parkinson’s Disease, vol. 10, no. s1. IOS Press BV, pp. S29–S36, 2020. doi: 10.3233/JPD-202135.

  • M. Höllerhage, “Secondary parkinsonism due to drugs, vascular lesions, tumors, trauma, and other insults,” Int Rev Neurobiol, vol. 149, pp. 377–418, Jan. 2019, doi: 10.1016/BS.IRN.2019.10.010.

  • D. V. Moretti, “Available and future treatments for atypical parkinsonism. A systematic review,” CNS Neuroscience and Therapeutics, vol. 25, no. 2. Blackwell Publishing Ltd, pp. 159–174, Feb. 01, 2019. doi: 10.1111/cns.13068.

  • H. Zhang, C. Li, W. Liu, J. Wang, J. Zhou, and S. Wang, “A multi-sensor wearable system for the quantitative assessment of Parkinson’s disease,” Sensors (Switzerland), vol. 20, no. 21, pp. 1–14, Nov. 2020, doi: 10.3390/s20216146.

  • Y. Titgemeyer et al., “Can commercially available wearable EEG devices be used for diagnostic purposes? An explorative pilot study,” Epilepsy and Behavior, vol. 103, Feb. 2020, doi: 10.1016/j.yebeh.2019.106507.

  • V. Pathak, K. Singh, A. Aziz, and A. Dhoot, “Efficient and Compressive IoT based Health Care System for Parkinson’s Disease Patient,” in Procedia Computer Science, 2020, vol. 167, pp. 1046–1055. doi: 10.1016/j.procs.2020.03.441.

  • N. Shawen et al., “Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors,” J Neuroeng Rehabil, vol. 17, no. 1, Apr. 2020, doi: 10.1186/s12984-020-00684-4.

  • M. Barrachina-Fernández, A. M. Maitín, C. Sánchez-ávila, and J. P. Romero, “Wearable technology to detect motor fluctuations in parkinson’s disease patients: Current state and challenges,” Sensors, vol. 21, no. 12. MDPI AG, Jun. 02, 2021. doi: 10.3390/s21124188.

  • A. Cufoglu, M. Lohi, and K. Madani, “A comparative study of selected classifiers with classification accuracy in user profiling,” in 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, 2009, vol. 3, pp. 708–712. doi: 10.1109/CSIE.2009.954.

  • I. Saritas, M. Koklu, K. Tutuncu, and S. Disease, “PERFORMANCE OF CLASSIFICATION TECHNIQUES ON PARKINSON DISEASE 69 PUBLICATIONS 771 CITATIONS SEE PROFILE PERFORMANCE OF CLASSIFICATION TECHNIQUES ON.” [Online]. Available: http://iraj.in

  • J. J. Rodríguez, L. I. Kuncheva, and C. J. Alonso, “Rotation Forest: A New Classifier Ensemble Method.”

  • Y. Liu, Y. Wang, and J. Zhang, “New Machine Learning Algorithm: Random Forest,” 2012.

  • T. Sathiya, R. Reenadevi, and B. Sathiyabhama, “Random Forest Classifier based detection of Parkinson’s disease,” 2021. [Online]. Available: http://annalsofrscb.ro

  • V. E. Balas, N. E. Mastorakis, M.-C. Popescu, and V. E. Balas, “Multilayer perceptron and neural networks,” 2009. [Online]. Available: https://www.researchgate.net/publication/228340819

  • F. Soleimanian Gharehchopogh and P. Mohammadi, “A Case Study of Parkinson Disease using Artificial Neural Network Deep Learning Artificial Neural Network View project An Optimization-based Learning Black Widow Optimization Algorithm for Text Psychology View project A Case Study of Parkinson’s disease Diagnosis using Artificial Neural Networks,” 2013. [Online]. Available: https://www.researchgate.net/publication/307557726

  • L. Bbeiman, “Bagging Predictors,” 1996.

  • M. S. bin Alam, M. J. A. Patwary, and M. Hassan, “Birth Mode Prediction Using Bagging Ensemble Classifier: A Case Study of Bangladesh,” in 2021 International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2021 - Proceedings, Feb. 2021, pp. 95–99. doi: 10.1109/ICICT4SD50815.2021.9396909.

  • A. K. Patra, R. Ray, A. A. Abdullah, and S. R. Dash, “Prediction of Parkinson’s disease using Ensemble Machine Learning classification from acoustic analysis,” in Journal of Physics: Conference Series, Nov. 2019, vol. 1372, no. 1. doi: 10.1088/1742-6596/1372/1/012041.

  • J. C. Platt, “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines Graph Embedding View project ClearType View project Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines,” 1998. [Online]. Available: https://www.researchgate.net/publication/2624239

  • Dr. B.C. Roy Engineering College. Department of Computer Science and Dr. B.C. Roy Engineering College. Department of Information Technology, 2012 National Conference on Computing and Communication Systems (NCCCS) : 21-22 November 2012 : Proceeding.

  • A. Szakál, IEEE Hungary Section, M. IEEE Systems, and Institute of Electrical and Electronics Engineers, SISY 2017 : IEEE 15th International Symposium on Intelligent Systems and Informatics : proceedings : September 14-16, 2017, Subotica, Serbia.

  • A. K. Tiwari, “Machine Learning Based Approaches for Prediction of Parkinson’s Disease,” Machine Learning and Applications: An International Journal, vol. 3, no. 2, pp. 33–39, Jun. 2016, doi: 10.5121/mlaij.2016.3203.

  • N. Saravanan and V. Gayathri, “Performance and Classification Evaluation of J48 Algorithm and Kendall’s Based J48 Algorithm (KNJ48),” International Journal of Computational Intelligence and Informatics, vol. 7, no. 4, 2018.

  • A. K. Shukla, P. Singh, and M. Vardhan, “Medical diagnosis of parkinson disease driven by multiple preprocessing technique with scarce lee silverman voice treatment data,” in Lecture Notes in Electrical Engineering, vol. 478, Springer Verlag, 2019, pp. 407–421. doi: 10.1007/978-981-13-1642-5_37.

  • N. Friedman, D. Geiger, G. Provan, P. Langley, and P. Smyth, “Bayesian Network Classifiers *,” Kluwer Academic Publishers, 1997.

  • Centurion University of Technology and Management, Institute of Electrical and Electronics Engineers. Bhubaneswar Subsection, and Institute of Electrical and Electronics Engineers, International Conference on Signal Processing, Communication, Power and Embedded System: (SCOPES)-2016 : 3rd-5th, October 2016 : IEEE conference proceedings.

  • N. Salmi and Z. Rustam, “Naïve Bayes Classifier Models for Predicting the Colon Cancer,” in IOP Conference Series: Materials Science and Engineering, Jul. 2019, vol. 546, no. 5. doi: 10.1088/1757-899X/546/5/052068.

  • R. Sulekh and A. Bhatia, “Predictive Model for Parkinson’s disease through Naïve Bayes Classification”.

  • “Detection of Parkinson Disease in Brain MRI using Convolutional Neural Network.” [Online]. Available: www.dicomapps.com/dicomtojpeg/index.html.


Abstract Views: 116

PDF Views: 0




  • Causes, Diagnosis And Prediction of Parkinson Diesease : A Review

Abstract Views: 116  |  PDF Views: 0

Authors

Gurpreet Singh
UIET, Panjab University, Chandigarh, India
Dr. Sukesha Sharma
UIET, Panjab University, Chandigarh, India

Abstract


In this review paper an introduction to Parkinson disease, stages in Parkinson disease, causes and the types of Parkinson disease has been discussed. Wearable devices or sensors that can be used for detection of Parkinson disease, checking the accuracy of the results or data provided by the wearable/portable sensors with clinical recorded data has also been discussed. Classifiers that can be used in machine learning for the detection of Parkinson disease have also been discussed. Methods by which Parkinson disease can be detected by medical lab tests have also been discussed. Also, we have discussed the prediction model of Parkinson disease using CNN in machine learning. All results are finally tabulated for comparison.

Keywords


Parkinson Disease, Wearable Devices, Machine Learning, Deep Learning, Machine Learning Classifiers.

References