A Two-stage Whale Optimization Method for Classification of Parkinson’s Disease Voice Recordings

Authors

DOI:

https://doi.org/10.18201/ijisae.2020261589

Keywords:

Parkinson disease, Machine Learning, Classification, Optimization

Abstract

The definitive treatment of Parkinson's disease, which causes movement disorders and has been increasing in recent years, is still not available today. However, effective studies are being conducted to improve the quality of life of the patients. In this study, a method for the efficient classification of data from Pakinson's disease and normal individuals is proposed. Since the dataset used in the proposed study consists of replicated samples, independence-based classifiers cannot be used for this dataset. When the distribution of the features in the dataset is examined, the success of the classical classifiers is very low due to the fact that the distribution centers between the clusters are very close to each other. Based on the basic idea that increasing the distance of the cluster centers from each other will increase the success, dimensionality techniques such as PCA, ICA, Relieff, RICA have been used. When the desired success was not achieved, a bond theory was established using a two-stage Whale optimization algorithm. Accordingly, the features of the three samples taken from an individual are closed to each other in the feature space, the total samples belonging to the same class are drawn to one side of the feature space and the feature space of the other class is positioned farthest from the center point. Thus, 3 different samples belonging to the same individual will be classified with the same label. In addition, since class difference will be high, classical classifiers such as SVM, k-NN LDA will be able to work successfully. The proposed method is compared with other techniques and as a result, it is seen that the representation ability in the property space is stronger than other related methods.

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References

A.E Lang, A.M. Lozano, Parkinson’s disease, New England Journal of Medicine 339(1998) 1044-1053.

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”, IEEE transactions on biomedical engineering, vol. 59(5), 2012.

J.R. Duffy, Motor Speech Disorders: Substrates, Differential Diagnosis, and Management, Elsevier, 2005.

Naranjo, L., Pérez, C. J., Campos-Roca, Y., & Martín, J. (2016). Addressing voice recording replications for Parkinson’s disease detection. Expert Systems with Applications, 46, 286-292.

Naranjo, L., Pérez, C. J., Martín, J., & Campos-Roca, Y. (2017). A two-stage variable selection and classification approach for Parkinson’s disease detection by using voice recording replications. Computer methods and programs in biomedicine, 142, 147-156.

Harel,B., Cannizzaro, M., &Snyder, P.J. (2004). Variability in fundamental frequency during speech in prodromal and incipient Parkinson’s disease: a longitudinal case study. Brain and Cognition,56,24–29.

Tsanas,A., Little, M.A., Mc Sharry, P.E., &Ramig, L.O.(2010). Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests. IEEE Transactions Biomedical Engineering, 57,884–893.

Little, M.A., McSharry, P.E., Hunter, E.J., Spielman, J., &Ramig, L.O. (2009). Suitability of dysphonia measurements for telemonitoring of Parkinson’sd isease.I EEETrans-actions on Biomedical Engineering, 56,1015–1022.

T. J. Wroge ; Y. Özkanca ; Cenk Demiroglu ; Dong Si ; David C. Atkins ; Reza Hosseini Ghomi, Parkinson’s Disease Diagnosis Using Machine Learning and Voice, 2018 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), December, 2018.

Hariharan M, Polat K, Sindhu R. A new hybrid intelligent system for accurate detection of Parkinson's disease, Computer Methods and Programs in Biomedicine, 113, 904-914, 2014.

Eskidere,O., Ertaç,F.,&Hanilçi,C.(2012). A comparison of regression methods for remote tracking of Parkinson’s disease progression. Expert Systems with Applications, 39, 5523–5528.

Orozco-Arroyave J.R., Arias-Londoño J.D., Vargas-Bonilla J.F., Nöth E. (2013) Analysis of Speech from People with Parkinson’s Disease through Nonlinear Dynamics. In: Drugman T., Dutoit T. (eds) Advances in Nonlinear Speech Processing. NOLISP 2013. Lecture Notes in Computer Science, vol 7911. Springer, Berlin, Heidelberg

M. Novotny , J. Rusz , R. Cmejla , E. Ruzicka ,Automatic evaluation of articulatory disorders in Parkinson’s disease, IEEE/ACM Trans. Audio Speech Lang. Process 22 (9) (2014) 1366–1378 .

Tan, T.Y., Zhang L., Lim C.P., Intelligent skin cancer diagnosis using improved particle swarm optimization and deep learning models, Applied Soft Computing, Volume 84, November 2019, 105725.

G.E. Güraksın, H. Haklı, H. Uğuz, “Support vector machines classification based on particle swarm optimization for bone age determination”, Applied Soft Computing, 24, 597-602, 2014.

Kohomri, B., Christodoulidis. A., Djerou., L., Babahenini. M. C., Cheriet. F., Retinal blood vessel segmentation using the elite-guided multi-objective artificial bee colony algorithm, IET Image Processing, Vol.12, Num.12, pp.2163-2171, 2018.

Subanya, B., Rajalaxmi R.R., Feature Selection using Artificial Bee Colony for Cardiovascular Disease Classification, International Conference on Electronics and Communication System (lCECS -2014), Coimbatore, india, 2014.

Li, L., Wang, J., SAR Image Ship Detection Based on Ant Colony Optimization, International Congress on Image and Signal Processing (CISP 2012), Chongging , China. 2012.

Weigo, X., The Weather Prediction Method Based on Artificial Immune System, International Forum on Information Technology and Applications, Kunming, Chinal , 2010.

Mirjalili, S. and Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, pp.51-67

https://archive.ics.uci.edu/ml/datasets/Parkinson+Dataset+with+replicated+acoustic+features+

Chen L. (2009) Curse of Dimensionality. In: LIU L., ÖZSU M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA.

Cunningham, P. 2007. Dimension Reduction. Technical Report. UCD-CSI-2007-7. University College Dublin.

Aapo Hyvainen, Erkki OJa, Juha Karhunen. "Independent Component Analysis", (1. ed.). New York: John wiley &sons, 2001.

C. M. Bishop. Pattern Recognition and Machine Learning, Springer, 2006.

J. Shlens, M. View, and I. Introduction, “A Tutorial on Principal Component Analysis,” 2014.

K. Kira, L.A. Rendell, A practical approach to feature selection, in: Proceedings of the Ninth International Workshop on Machine Learning, 1992b, pp. 249–256.

Urbanowicz R.J., Meeker M., Cava W., Olson R.S., Moore J.H., Relief-based feature selection: Introduction and review, Journal of Biomedical Informatics, Vol 85, pp.189-203, 2018.

C.-Y. Liou, W.-C. Cheng, J.-W. Liou, and D.-R. Liou, “Autoencoder for words,” Neurocomputing, vol. 139, pp. 84–96, 2014.

Yang W., Wang K., Zuo W., Neighborhood Component Feature Selection for High-Dimensional Data, Journal Of Computers, Vol. 7, No. 1, January 2012.

Quoc V Le, Alexandre Karpenko, Jiquan Ngiam, and Andrew Y Ng. Ica with reconstruction cost for efficient overcomplete feature learning. Advances in Neural Information Processing Systems, 24:1017–1025, 2011.

C. Cortes and V. Vapnik, “Support Vector Networks,” Machine Learning, vol. 20, pp. 273-297, 1995.

K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. San Diego, CA: Academic, 1990.

Mao, C., Hu, B., Wang, M. and Moore, P., “Learning from neighborhood for classification with local distribution characteristics”, IEEE International Joint Conference on Neural Networks (IJCNN),1-8(2015).

Özkaya, U., & Seyfi, L. “Dimension Forecast in Microstrip Antenna for C/X/Ku Band by Artificial Neural Network”, 2nd International Symposium on Innovative Approaches in Scientific Studies, vol. 3, pp. 518-522 (2018).

Özkaya, U., Öztürk, Ş., Seyfi, L., & Akdemir, B. “Non-Magnetic Materials Assignment based on Artificial Neural Network”, 1st International Symposium on Innovative Approaches in Scientific Studies, vol. 2, pp. 410-415 (2018)

Ozkaya, U., & Seyfi, L. (2018). A comparative study on parameters of leaf-shaped patch antenna using hybrid artificial intelligence network models. Neural Computing and Applications, 29(8), 35-45.

Ozkaya, U., & Seyfi, L. (2015). Dimension optimization of microstrip patch antenna in X/Ku band via artificial neural network. Procedia-Social and Behavioral Sciences, 195, 2520-2526.

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Published

26.06.2020

How to Cite

Ozturk, S., & Unal, Y. (2020). A Two-stage Whale Optimization Method for Classification of Parkinson’s Disease Voice Recordings. International Journal of Intelligent Systems and Applications in Engineering, 8(2), 84–93. https://doi.org/10.18201/ijisae.2020261589

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Research Article