i-manager Publications

A Review on Parkinson's Disease Diagnosis using Machine Learning Techniques

Chnachal*, Megha Mishra**, Vishnu Kumar Mishra***

*-*** Department of Computer Science, Shri Shankaracharya Engineering College, Shri Shankaracharya Group of Institutions, Chhattisgarh, India.

Periodicity:January - March'2023
DOI : https://doi.org/10.26634/jip.10.1.19381

Abstract

The decreased production of dopamine in the forebrain is believed to be the underlying cause of Parkinson's disease, a neurodegenerative disorder that affects the nervous system. Parkinson's disease is a chronic and progressive illness that may develop new symptoms over time (Nilashi et al., 2016). This occurs as neurons in the substantia nigra of the brain gradually die. People with Parkinson's disease may find it difficult to perform everyday tasks in the workplace. Although clinical evaluations consider a significant amount of data that includes various aspects, it is not always easy to determine whether a person has PD based on this data alone. Feature selection methods can help address this issue. Various techniques are being researched, developed, and evaluated for diagnosing Parkinson's disease, based on the relevant information. This study provides an overview of the use of machine learning algorithms to predict Parkinson's disease, as well as the various new technologies that have been developed and the accuracy that has been achieved.

Keywords

PD (Parkinson Disease), Dopamine, SVM (Support Vector Machine), KNN (K Nearest Neighbor), ANN (Artificial Neural Network).

How to Cite this Article?

Chnachal, Mishra, M., and Mishra, V. K. (2023). A Review on Parkinson's Disease Diagnosis using Machine Learning Techniques. i-manager’s Journal on Image Processing , 10(1), 21-28. https://doi.org/10.26634/jip.10.1.19381

References

[1]. Ahlrichs, C., & Lawo, M. (2013). Parkinson's disease motor symptoms in machine learning: A review. Health Informatics: An International Journal (HIIJ), 2(4), 1-18.

[2]. Ahmadlou, M., & Adeli, H. (2010). Enhanced probabilistic neural network with local decision circles: A robust classifier. Integrated Computer-Aided Engineering, 17(3), 197-210.

[3]. Aich, S., Kim, H. C., Hui, K. L., Al-Absi, A. A., & Sain, M. (2019, February). A supervised machine learning approach using different feature selection techniques on voice datasets for prediction of Parkinson's disease. In 2019 21st International Conference on Advanced Communication Technology (ICACT) 7(3), 1116-1121. IEEE.

[4]. Al-Fatlawi, A. H., Jabardi, M. H., & Ling, S. H. (2016, July). Efficient diagnosis system for Parkinson's disease using deep belief network. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 1324-1330). IEEE.

[5]. Åström, F., & Koker, R. (2011). A parallel neural network approach to prediction of Parkinson's disease. Expert Systems with Applications, 38(10), 12470-12474.

[6]. Avci, D., & Dogantekin, A. (2016). An expert diagnosis system for Parkinson disease based on genetic algorithmwavelet kernel-extreme learning machine. Parkinson's Disease, 2016.

[7]. Bhat, S., Acharya, U. R., Hagiwara, Y., Dadmehr, N., & Adeli, H. (2018). Parkinson's disease: Cause factors, measurable indicators, and early diagnosis. Computers in Biology and Medicine, 102, 234-241.

[8]. Bhattacharya, I., & Bhatia, M. P. S. (2010). SVM classification to distinguish Parkinson disease patients. In Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India (pp. 1-6).

[9]. Cauwenberghs, G., & Poggio, T. (2000). Incremental and decremental support vector machine learning. Advances in Neural Information Processing Systems, 13.

[10]. Chen, H. L., Huang, C. C., Yu, X. G., Xu, X., Sun, X., Wang, G., & Wang, S. J. (2013). An efficient diagnosis system for detection of Parkinson's disease using fuzzy knearest neighbor approach. Expert Systems with Applications, 40(1), 263-271.

[11]. Chen, H. L., Wang, G., Ma, C., Cai, Z. N., Liu, W. B., & Wang, S. J. (2016). An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson ׳ s disease. Neurocomputing, 184, 131-144.

[12]. Cho, C. W., Chao, W. H., Lin, S. H., & Chen, Y. Y. (2009). A vision-based analysis system for gait recognition in patients with Parkinson's disease. Expert Systems with Applications, 36(3), 7033-7039.

[13]. Das, R. (2010). A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Systems with Applications, 37(2), 1568-1572.

[14]. Delenclos, M., Jones, D. R., McLean, P. J., & Uitti, R. J. (2016). Biomarkers in Parkinson's disease: Advances and strategies. Parkinsonism & Related Disorders, 22, S106-S110.

[15]. Ene, M. (2008). Neural network-based approach to discriminate healthy people from those with Parkinson's disease. Annals of the University of Craiova-Mathematics and Computer Science Series, 35, 112-116.

[16]. Ericsson, A., Lonsdale, M. N., Astrom, K., Edenbrandt, L., & Friberg, L. (2005). Decision support system for the diagnosis of Parkinson's disease. In Image Analysis: 14th Scandinavian Conference, SCIA 2005, Joensuu, Finland, June 19-22, 2005. Proceedings 14 (pp. 740-749). Springer Berlin Heidelberg.

[17]. Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., & Acharya, U. R. (2018). Deep learning for healthcare applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine, 161, 1-13.

[18]. Gil, D., & Johnsson, M. (2009). Diagnosing Parkinson by using artificial neural networks and support vector machines. Global Journal of Computer Science and Technology, 9(4), 63-71.

[19]. Gupta, D., Julka, A., Jain, S., Aggarwal, T., Khanna, A., Arunkumar, N., & de Albuquerque, V. H. C. (2018). Optimized cuttlefish algorithm for diagnosis of Parkinson's disease. Cognitive Systems Research, 52, 36-48.

[20]. Hariharan, M., Polat, K., & 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.

[21]. Hirschauer, T. J., Adeli, H., & Buford, J. A. (2015). Computer-aided diagnosis of Parkinson's disease using enhanced probabilistic neural network. Journal of Medical Systems, 39, 1-12.

[22]. Islam, M. S., Parvez, I., Deng, H., & Goswami, P. (2014, May). Performance comparison of heterogeneous classifiers for detection of Parkinson's disease using voice disorder (dysphonia). In 2014 International Conference on Informatics, Electronics & Vision (ICIEV) (pp. 1-7). IEEE.

[23]. Lima, D. W. C., Ferreira, L. A., Vieira, A. N., Azevedo, L. D. S., Silva, A. P., da Cunha, B. M. C., & Sousa, L. C. A. (2018). Ditos sobre o uso abusivo de álcool e outras drogas: significados e histórias de vida. SMAD, Revista Eletrônica Saúde Mental Álcool e Drogas (Edição em Português), 14(3), 151-158.

[24]. Lipton, Z. C., Kale, D., & Wetzel, R. (2016, December). Directly modeling missing data in sequences with rnns: Improved classification of clinical time series. In Machine Learning for Healthcare Conference (pp. 253-270). PMLR.

[25]. Little, M., McSharry, P., Hunter, E., Spielman, J., & Ramig, L. (2009). Suitability of dysphonia measurements for telemonitoring of Parkinson's disease. IEEE Transactions on Biomedical Engineering, 56(4), 1015-1022.

[26]. Liu, W., Liu, J., Peng, T., Wang, G., Balas, V. E., Geman, O., & Chiu, H. W. (2022). Prediction of Parkinson's disease based on artificial neural networks using speech datasets. Journal of Ambient Intelligence and Humanized Computing, 1-14.

[27]. Mandal, I., & Sairam, N. (2014). New machinelearning algorithms for prediction of Parkinson's disease. International Journal of Systems Science, 45(3), 647-666.

[28]. Mostafa, S. A., Mustapha, A., Mohammed, M. A., Hamed, R. I., Arunkumar, N., Abd Ghani, M. K., ... & Khaleefah, S. H. (2019). Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson's disease. Cognitive Systems Research, 54, 90-99.

[29]. National Parkinson Foundation. (n.d.). What is Parkinson's?

[30]. Nilashi, M., Ibrahim, O., & Ahani, A. (2016). Accuracy improvement for predicting Parkinson's disease progression. Scientific Reports, 6(1), 34181.

[31]. Oung, Q. W., Muthusamy, H., Basah, S. N., Lee, H., & Vijean, V. (2018). Empirical wavelet transform based features for classification of Parkinson's disease severity. Journal of Medical Systems, 42, 1-17.

[32]. Pagan, F. L. (2012). Improving outcomes through early diagnosis of Parkinson's disease. American Journal of Managed Care, 18(7), 176-182.

[33]. Passos, L. A., Pereira, C. R., Rezende, E. R., Carvalho, T. J., Weber, S. A., Hook, C., & Papa, J. P. (2018, May). Parkinson disease identification using residual networks and optimum-path forest. In 2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI) (pp. 325-330).

[34]. Peng, B., Zhou, Z., Geng, C., Tong, B., Zhou, Z., Zhang, T., & Dai, Y. (2016, October). Computer aided analysis of cognitive disorder in patients with Parkinsonism using machine learning method with multilevel ROI based features. In 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (pp. 1792-1796). IEEE.

[35]. Prashanth, R., Roy, S. D., Mandal, P. K., & Ghosh, S. (2016). High-accuracy detection of early Parkinson's disease through multimodal features and machine learning. International Journal of Medical Informatics, 90, 13-21.

[36]. Rovini, E., Maremmani, C., Moschetti, A., Esposito, D., & Cavallo, F. (2018). Comparative motor pre-clinical assessment in Parkinson's disease using supervised machine learning approaches. Annals of Biomedical Engineering, 46, 2057-2068.

[37]. Sakar, C. O., & Kursun, O. (2010). Telediagnosis of Parkinson's disease using measurements of dysphonia. Journal of Medical Systems, 34, 591-599.

[38]. Schönweiler, R., Hess, M., Wübbelt, P., & Ptok, M. (2000). Novel approach to acoustical voice analysis using artificial neural networks. Journal of the Association for Research in Otolaryngology, 1, 270-282.

[39]. Sriram, T. V., Rao, M. V., Narayana, G. S., & Kaladhar, D. S. V. G. K. (2015). Diagnosis of Parkinson disease using machine learning and data mining systems from voice dataset. In Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014, 1 (pp. 151-157). Springer International Publishing.

[40]. Surathi, P., Jhunjhunwala, K., Yadav, R., & Pal, P. K. (2016). Research in Parkinson's disease in India: A review. Annals of Indian Academy of Neurology, 19(1), 9-20.

[41]. Tsanas, A., Little, M. A., McSharry, P. E., Spielman, J., & Ramig, L. O. (2012). Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease. IEEE Transactions on Biomedical Engineering, 59(5), 1264-1271.

[42]. Zeinali, Y., & Story, B. A. (2017). Competitive probabilistic neural network. Integrated Computer-Aided Engineering, 24(2), 105-118.

A Review on Parkinson's Disease Diagnosis using Machine Learning Techniques

Abstract

Keywords

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Options for accessing this content:

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	35	35	200	15
Pdf & Online	35	35	400	25