research-article

A Comparative Study of Parkinson Disease Diagnosis in Machine Learning

Authors:

Mohammed Younis Thanoun,

MOHAMMAD T. YASEENAuthors Info & Claims

ICAAI '20: Proceedings of the 4th International Conference on Advances in Artificial Intelligence

Pages 23 - 28

https://doi.org/10.1145/3441417.3441425

Published: 04 February 2021 Publication History

Abstract

Parkinson's disease (PD) is a cumulative disorder in the nervous system. PD patients may experience difficulty in movement and speaking due to damages in certain parts in the brain. In this study, we propose using two types of Ensemble learning methods Stacking Classifier and voting classifier, which are potential methods of PD detection using machine learning. Then, we compared between the results of both of them. Stacking Classifier method outperformed voting classifier and the obtained accuracy was 92.2% and 83.57%, respectively. This comparative study would help come out with higher detection accuracy for medical applications such as this chronic disease.

References

[1]

Pillon, B., Dubois, B., Cusimano, G., Bonnet, A.-M., Lhermitte, F., & Agid, Y. (1989). Does cognitive impairment in Parkinson's disease result from non-dopaminergic lesions? Journal of Neurology, Neurosurgery & Psychiatry, 52(2), 201-206.

[2]

Bloem, B.R. and M. Munneke, Revolutionising management of chronic disease: The ParkinsonNet approach. Bmj, 2014. 348: p. g1838.

[3]

H.-L. Chen, G. Wang, C. Ma, Z.-N. Cai, W.-B. Liu, and S.-J. Wang, "An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳ s disease," Neurocomputing, vol. 184, pp. 131-144, 2016.

Digital Library

[4]

D. Calne, B. Snow, and C. Lee, "Criteria for diagnosing Parkinson's disease," Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, vol. 32, no. S1, pp. S125-S127, 1992.

[5]

H.-L. Chen, "An efficient diagnosis system for detection of Parkinson's disease using fuzzy k-nearest neighbor approach," Expert systems with applications, vol. 40, no. 1, pp. 263-271, 2013.

[6]

C. Sommer and D. W. Gerlich, "Machine learning in cell biology–teaching computers to recognize phenotypes," J Cell Sci, vol. 126, no. 24, pp. 5529-5539, 2013.

[7]

M. Nilashi, O. Bin Ibrahim, A. Mardani, A. Ahani, and A. Jusoh, "A soft computing approach for diabetes disease classification," Health Informatics Journal, vol. 24, no. 4, pp. 379-393, 2018.

[8]

S. Ozer, "Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI," Medical physics, vol. 37, no. 4, pp. 1873-1883, 2010.

[9]

Nilashi, M., A soft computing approach for diabetes disease classification. Health Informatics Journal, 2018. 24(4): p. 379-393.

[10]

Polikar, R. (2006). Ensemble based systems in decision making. IEEE Circuits and systems magazine, 6(3), 21-45.

[11]

Ö. Eskidere, F. Ertaş, and C. Hanilçi, "A comparison of regression methods for remote tracking of Parkinson's disease progression," Expert Systems with Applications, vol. 39, no. 5, pp. 5523-5528, 2012.

Digital Library

[12]

M. Hariharan, K. Polat, and R. Sindhu, "A new hybrid intelligent system for accurate detection of Parkinson's disease," Computer methods and programs in biomedicine, vol. 113, no. 3, pp. 904-913, 2014.

[13]

T. Peterek, P. Dohnálek, P. Gajdoš, and M. Šmondrk, "Performance evaluation of Random Forest regression model in tracking Parkinson's disease progress," in 13th International Conference on Hybrid Intelligent Systems (HIS 2013), 2013: IEEE, pp. 83-87.

[14]

W.-L. Zuo, Z.-Y. Wang, T. Liu, and H.-L. Chen, "Effective detection of Parkinson's disease using an adaptive fuzzy k-nearest neighbor approach," Biomedical Signal Processing and Control, vol. 8, no. 4, pp. 364-373, 2013.

[15]

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

[16]

V. Despotovic, T. Skovranek, and C. Schommer, "Speech Based Estimation of Parkinson's Disease Using Gaussian Processes and Automatic Relevance Determination," Neurocomputing, 2020.

[17]

K. Polat, "A Hybrid Approach to Parkinson Disease Classification Using Speech Signal: The Combination of SMOTE and Random Forests," in 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), 2019: IEEE, pp. 1-3.

[18]

Sakar, C. O., Serbes, G., Gunduz, A., Tunc, H. C., Nizam, H., Sakar, B. E., Apaydin, H. (2019). A comparative analysis of speech signal processing algorithms for Parkinson's disease classification and the use of the tunable Q-factor wavelet transform. Applied Soft Computing, 74, 255-263.

[19]

Froelich, W., K. Wrobel, and P. Porwik, Diagnosis of Parkinson's disease using speech samples and threshold-based classification. Journal of Medical Imaging and Health Informatics, 2015. 5(6): p. 1358-1363.

[20]

Logemann, J.A., Frequency and cooccurrence of vocal tract dysfunctions in the speech of a large sample of Parkinson patients. Journal of Speech and hearing Disorders, 1978. 43(1): p. 47-57.

[21]

Parisi, L., N. RaviChandran, and M.L. Manaog, Feature-driven machine learning to improve early diagnosis of Parkinson's disease. Expert Systems with Applications, 2018. 110: p. 182-190.

[22]

Xie, Y., Y. Liu, and Q. Fu, Imbalanced data sets classification based on SVM for sand-dust storm warning. Discrete Dynamics in Nature and Society, 2015. 2015.

[23]

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.

[24]

Tsoumakas, G., Katakis, I., & Vlahavas, I. (2004). Effective voting of heterogeneous classifiers. Paper presented at the European Conference on Machine Learning.

[25]

Sikora, R. (2015). A modified stacking ensemble machine learning algorithm using genetic algorithms Handbook of Research on Organizational Transformations through Big Data Analytics (pp. 43-53): IGi Global.

[26]

A. Tsanas, M. A. Little, P. E. McSharry, J. Spielman and L. O. Ramig, "Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease," in IEEE Transactions on Biomedical Engineering, vol. 59, no. 5, pp. 1264-1271, May 2012.

[27]

B. Pittman, R. H. Ghomi and D. Si, "Parkinson's Disease Classification of mPower Walking Activity Participants," 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, 2018, pp. 4253-4256.

[28]

S. Magotra and K. Kumar, “Detection of HELLO flood attack on LEACH protocol,” in Proceedings of the IEEE International Advance Computing Conference (IACC '14), pp. 193–198, Gurgaon, India, 2014.

[29]

M.A. Little, P.E. McSharry, E.J. Hunter, J. Spielman, L.O. Ramig. Suitability of dysphonia measurements for telemonitoring of Parkinson‟s disease, IEEE Trans. Biomedical Engineering 56(4), 1015-1022, 2009.

[30]

S. Sapir, L. Ramig, J. Spielman, C. Fox, Formant Centralization Ratio (FCR): A proposal for a new acoustic measure of dysarthric speech. Journal of Speech Language and Hearing Research, 53, 114-25, 2010.

[31]

A. Tsanas, M.A. Little, P.E. McSharry, L.O. Ramig, Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson‟s disease symptom severity, Journal of the Royal Society Interface, Vol. 8, 842-855, 2011.

[32]

I.R. Titze. Principles of Voice Production. National Center for Voice and Speech, Iowa City, US, 2nd ed., 2000.

[33]

Aida-zade K, Xocayev A,Rustamov S. Speech recognition usingsupport vector machines. In: AICT’16.10th IEEE International Conference on Application of Information and Communication Technologies; 2016

[34]

Tirumala SS, Shahamiri SR,Garhwal AS, Wang R. Speaker identification features extraction methods: A systematic review. Expert Systems with Applications. 2017; 90:250-271

[35]

V. Nigam, S. Jain, and K. Burse, “Profile based scheme against DDoS attack in WSN,” in Proceedings of the 4th International Conference on Communication Systems and Network Technologies (CSNT '14), pp. 112–116, Bhopal, India, 2014.

Digital Library

[36]

Krassovitskiy A, Mussabayev R. Energy-based centroid identification and cluster propagation with noise detection. In: Nguyen N, Pimenidis E, Khan Z, Trawiński B, editors. Computational Collective Intelligence. Lecture Notes in Computer Science. Vol. 11055. Cham: Springer; 2018. pp. 523-533. 319-98443-8_48. ICCCI 2018

Digital Library

[37]

P. Yi, T. Zhu, Q. Zhang, Y. Wu, and J. H. Li, “A denial of service attack in advanced metering infrastructure network,” in Proceedings of the IEEE International Conference on Communications (ICC '14), Sydney, Australia, June 2014.

Cited By

Kapila RSaleti S(2024)Optimizing Predictive Models for Parkinson's Disease DiagnosisIntelligent Technologies and Parkinson’s Disease10.4018/979-8-3693-1115-8.ch015(255-275)Online publication date: 23-Feb-2024
https://doi.org/10.4018/979-8-3693-1115-8.ch015
Aldhyani TAl-Nefaie AKoundal D(2024)Modeling and diagnosis Parkinson disease by using hand drawing: deep learning modelAIMS Mathematics10.3934/math.20243349:3(6850-6877)Online publication date: 2024
https://doi.org/10.3934/math.2024334
Keserwani PDas SSarkar N(2024)A comparative study: prediction of parkinson’s disease using machine learning, deep learning and nature inspired algorithmMultimedia Tools and Applications10.1007/s11042-024-18186-z83:27(69393-69441)Online publication date: 31-Jan-2024
https://doi.org/10.1007/s11042-024-18186-z
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ICAAI '20: Proceedings of the 4th International Conference on Advances in Artificial Intelligence

October 2020

102 pages

ISBN:9781450387842

DOI:10.1145/3441417

Copyright © 2020 ACM.

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Published: 04 February 2021

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ICAAI 2020: 2020 The 4th International Conference on Advances in Artificial Intelligence

October 9 - 11, 2020

London, United Kingdom

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Cited By

Kapila RSaleti S(2024)Optimizing Predictive Models for Parkinson's Disease DiagnosisIntelligent Technologies and Parkinson’s Disease10.4018/979-8-3693-1115-8.ch015(255-275)Online publication date: 23-Feb-2024
https://doi.org/10.4018/979-8-3693-1115-8.ch015
Aldhyani TAl-Nefaie AKoundal D(2024)Modeling and diagnosis Parkinson disease by using hand drawing: deep learning modelAIMS Mathematics10.3934/math.20243349:3(6850-6877)Online publication date: 2024
https://doi.org/10.3934/math.2024334
Keserwani PDas SSarkar N(2024)A comparative study: prediction of parkinson’s disease using machine learning, deep learning and nature inspired algorithmMultimedia Tools and Applications10.1007/s11042-024-18186-z83:27(69393-69441)Online publication date: 31-Jan-2024
https://doi.org/10.1007/s11042-024-18186-z
Samad ADhanda NVerma R(2024)Parkinson’s Disease Detection Using Machine LearningProceedings of the Fifth International Conference on Trends in Computational and Cognitive Engineering10.1007/978-981-97-1923-5_14(183-192)Online publication date: 14-Jun-2024
https://doi.org/10.1007/978-981-97-1923-5_14
Rehman ASaba TMujahid MAlamri FElHakim N(2023)Parkinson’s Disease Detection Using Hybrid LSTM-GRU Deep Learning ModelElectronics10.3390/electronics1213285612:13(2856)Online publication date: 28-Jun-2023
https://doi.org/10.3390/electronics12132856
El Ariss OHu K(2023)ResNet-Based Parkinson's Disease ClassificationIEEE Transactions on Artificial Intelligence10.1109/TAI.2022.31936514:5(1258-1268)Online publication date: Oct-2023
https://doi.org/10.1109/TAI.2022.3193651
Al-Husban AAbdulridha MMohamad AIbrahim A(2022)Biocomposite’s Multiple Uses for a New Approach in the Diagnosis of Parkinson’s Disease Using a Machine Learning AlgorithmAdsorption Science & Technology10.1155/2022/61593922022Online publication date: 14-Jul-2022
https://doi.org/10.1155/2022/6159392
Reddy CKanchana M(2022)Artificial Intelligence towards Parkinson’s disease Diagnosis: A systematic Review of Contemporary Literature2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA)10.1109/ICCCMLA56841.2022.9988755(31-37)Online publication date: 8-Oct-2022
https://doi.org/10.1109/ICCCMLA56841.2022.9988755
Saravanan SRamkumar KAdalarasu KSivanandam VKumar SStalin SAmirtharajan R(2022)A Systematic Review of Artificial Intelligence (AI) Based Approaches for the Diagnosis of Parkinson’s DiseaseArchives of Computational Methods in Engineering10.1007/s11831-022-09710-129:6(3639-3653)Online publication date: 20-Jan-2022
https://doi.org/10.1007/s11831-022-09710-1
Werner de Vargas VSchneider Aranda Jdos Santos Costa Rda Silva Pereira PVictória Barbosa J(2022)Imbalanced data preprocessing techniques for machine learning: a systematic mapping studyKnowledge and Information Systems10.1007/s10115-022-01772-865:1(31-57)Online publication date: 9-Nov-2022
https://dl.acm.org/doi/10.1007/s10115-022-01772-8

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