Voice feature Selection using genetic algorithms for detecting Parkinson’s disease
DOI:
https://doi.org/10.36825/RITI.10.21.013Keywords:
Parkinson, Machine Learning Algorithms, Genetic Algorithm, SVMAbstract
Today, Parkinson’s Disease (PD) is one of the most common neurodegenerative diseases in the world after Alzheimer’s disease. About 6.2 million people have it and it is estimated that by 2040 the number of Parkinson’s patients will double it. PD reduces motor function, which is why patients suffer from decreased movement, stiffness, tremors and even the voice and speech production including breathing, articulation, and phonation. For this reason, the voice features of patients vary in comparison to people who do not have PD. Therefore, we are looking for a method that allows us to select the features of the voice that affects the most to the patient's diagnosis. There are several methods for feature selection and in our case, we use genetic algorithms (GA). To validate our feature selection approach, we constructed an SVM classifier where the best accuracy of 88.54% was achieved with 8 features selected by GA.
References
Martínez-Fernández, R., Gasca-Salas, C., Sánchez-Ferro, A., Obeso, J. Á. (2016). Actualización en la Enfermeda de Parkinson. Revista Médica Clínica Los Condes, 27 (3), 363-379. https://doi.org/10.1016/j.rmclc.2016.06.010
Delgado Hernández, J., Izquierdo Arteaga, L. M. (2016). Eficacia de la rehabilitación de la voz en etapas tempranas de la Enfermedad de Parkinson. Revista Discapacidad Clínica Neurociencias, 3 (1), 42–47. https://doi.org/10.14198/DCN.2016.3.1.04
Martínez-Sánchez, F. (2010). Speech and voice disorders in Parkinson’s disease. Revista de Neurología, 51 (9), 542–550. https://doi.org/10.33588/rn.5109.2009509
Luukka, P. (2011). Feature selection using fuzzy entropy measures with similarity classifier. Expert Systems with Applications, 38 (4), 4600–4607. https://doi.org/10.1016/j.eswa.2010.09.133
Shahbaba, B., Neal, R. (2009). Nonlinear models using dirichlet process mixtures. Journal of Machine Learning Research, 10, 1829–1850. https://www.jmlr.org/papers/volume10/shahbaba09a/shahbaba09a.pdf
Ozcift, A. (2012). SVM feature selection based rotation forest ensemble classifiers to improve computer-Aided diagnosis of Parkinson disease. Journal of Medical Systems, 36 (4), 2141–2147. https://doi.org/10.1007/s10916-011-9678-1
Little, M. A., McSharry, P. E., Hunter, E. J., Spielman, J., Ramig, L. O. (2009). Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Transactions on Biomedical Engineering, 56 (4), 1015–1022. https://doi.org/10.1109/TBME.2008.2005954
Mostafa, S. A., Mustapha, A., Mohammed, M. A., Hamed, R. I., Arunkumar, N., Ghani, M. K. A., Jaber, M. M., 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. https://doi.org/10.1016/j.cogsys.2018.12.004
Karapinar Senturk, Z. (2020). Early diagnosis of Parkinson’s disease using machine learning algorithms. Medical Hypotheses, 138, 1-5. https://doi.org/10.1016/j.mehy.2020.109603
Sivaranjini, S., Sujatha, C. M. (2020). Deep learning based diagnosis of Parkinson’s disease using convolutional neural network. Multimedia Tools and Applications, 79, 15467–15479. https://doi.org/10.1007/s11042-019-7469-8
Das, R. (2010). A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Systems with Applications, 37 (2), 1568–1572. https://doi.org/10.1016/j.eswa.2009.06.040
Mei, J., Desrosiers, C., Frasnelli, J. (2021). Machine Learning for the Diagnosis of Parkinson’s Disease: A Review of Literature. Frontiers in Aging Neuroscience, 13, 1–41. https://doi.org/10.3389/fnagi.2021.633752
Arefi Shirvan, R., Tahami, E. (2011). Voice analysis for detecting Parkinson's disease using genetic algorithm and KNN classification method. 18th Iranian Conference on BioMedical Engineering (ICBME), Tehran, Iran. https://doi.org/10.1109/ICBME.2011.6168572
Tsanas, A., Little, M. A., McSharry, P. E., Ramig, L. O. (2010). Enhanced classical dysphonia measures and sparse regression for telemonitoring of Parkinson’s disease progression. IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, TX, USA. https://doi.org/10.1109/ICASSP.2010.5495554
Polat, K. (2012). Classification of Parkinson’s disease using feature weighting method on the basis of fuzzy C-means clustering. International Journal of Systems Science, 43 (4), 597–609. https://doi.org/10.1080/00207721.2011.581395
Gestal, M., Rivero, D., Rabuñal, J. R., Dorado, J. Pazos, A. (2010). Introducción a los Algoritmos Genéticos y la Programación Genética. Universidade da Coruña.
Aguado González, E. (2017). Detección Automática De Anomalías en Patrullaje Robotizado [Tesis de Grado]. Universidad Politécnica de Madrid. https://oa.upm.es/49198/
van der Maaten, L., Hinton, G. (2008). Visualizing Data using t-SNE Laurens. Journal of Machine Learning Research, 9 (86), 2579-2605. https://www.jmlr.org/papers/v9/vandermaaten08a.html
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