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.
- 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.Google Scholar
- Bloem, B.R. and M. Munneke, Revolutionising management of chronic disease: The ParkinsonNet approach. Bmj, 2014. 348: p. g1838.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- S. Ozer , "Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI," Medical physics, vol. 37, no. 4, pp. 1873-1883, 2010.Google ScholarCross Ref
- Nilashi, M., , A soft computing approach for diabetes disease classification. Health Informatics Journal, 2018. 24(4): p. 379-393.Google ScholarCross Ref
- Polikar, R. (2006). Ensemble based systems in decision making. IEEE Circuits and systems magazine, 6(3), 21-45.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google Scholar
- V. Despotovic, T. Skovranek, and C. Schommer, "Speech Based Estimation of Parkinson's Disease Using Gaussian Processes and Automatic Relevance Determination," Neurocomputing, 2020.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google Scholar
- Tsoumakas, G., Katakis, I., & Vlahavas, I. (2004). Effective voting of heterogeneous classifiers. Paper presented at the European Conference on Machine Learning.Google Scholar
- 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.Google Scholar
- 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, doi: 10.1109/TBME.2012.2183367.Google Scholar
- 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, doi: 10.1109/EMBC.2018.8513409.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- I.R. Titze. Principles of Voice Production. National Center for Voice and Speech, Iowa City, US, 2nd ed., 2000.Google Scholar
- 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; 2016Google Scholar
- Tirumala SS, Shahamiri SR,Garhwal AS, Wang R. Speaker identification features extraction methods: A systematic review. Expert Systems with Applications. 2017; 90:250-271Google Scholar
- 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.Google ScholarDigital Library
- 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. DOI: 10.1007/978-3- 319-98443-8_48. ICCCI 2018Google ScholarDigital Library
- 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.Google ScholarCross Ref
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