Abstract
Parkinson disease(PD) is a neurological disorder which affect the nervous system of the body causing problem related to gait and speech disorder. Speech and gait serve as major parameter in diagnosis of the disease in the early stages of its symptoms. This study uses these parameters to perform a comparative study of two nature inspired algorithm for diagnosis of the Parkinson Disease. The process involves first selecting an optimal feature set for classification and then using them to classify and predict PD patients from non PD patients. Two different datasets were used consisting of gait and speech data of PD and non PD patients. Optimal Feature selection was done using Particle swarm optimization and Egyptian Vulture Optimization Algorithm. The optimal feature set was then used to classify the dataset using KNN classifier. According to the experiment EVOA outperforms PSO in the selection of the feature subset. This study thus concludes that new meta-heuristic algorithm EVOA works better than traditional PSO in diagnosis of PD patients which in real life can help to speed up the process and lessen the suffering of the patient by early detection.
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Jankovic, J.: Parkinsons disease: clinical features and diagnosis. J. Neurol. Neurosurg. Psychiatry 79(4), 368–376 (2008)
Langston, J.W.: Parkinsons disease: current and future challenges. Neurotoxicology 23(4), 443–450 (2002)
Martínez-Martín, P., Gil-Nagel, A., Gracia, L.M., Gómez, J.B., Martínez-Sarriés, J., Bermejo, F.: Unified parkinson’s disease rating scale characteristics and structure. Mov. Disord. 9(1), 76–83 (1994)
Ni, J., Liang, L.: A gait recognition method based on KFDA and SVM. In: 2009 International Workshop on Intelligent Systems and Applications, ISA 2009, pp. 1–4. IEEE (2009)
Klucken, J., Barth, J., Kugler, P., Schlachetzki, J., Henze, T., Marxreiter, F., Kohl, Z., Steidl, R., Hornegger, J., Eskofier, B., et al.: Unbiased and mobile gait analysis detects motor impairment in Parkinsons disease. PloS ONE 8(2), e56956 (2013)
Sakar, B.E., Isenkul, M.E., Sakar, C.O., Sertbas, A., Gurgen, F., Delil, S., Apaydin, H., Kursun, O.: Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J. Biomed. Health Inform. 17(4), 828–834 (2013)
Liu, H., Motoda, H.: Feature Extraction, Construction, Selection: A Data Mining Perspective. Springer, New York (1998)
Xue, B., Zhang, M., Browne, W.N.: Multi-objective particle swarm optimisation (pso) for feature selection. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, pp. 81–88. ACM (2012)
Xue, B., Zhang, M., Dai, Y., Browne, W.N.: PSO for feature construction and binary classification. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, pp. 137–144. ACM (2013)
Liu, Y., Wang, G., Chen, H., Dong, H., Zhu, X., Wang, S.: An improved particle swarm optimization for feature selection. J. Bionic Eng. 8(2), 191–200 (2011)
Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, New York (2010)
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108. IEEE (1997)
Sur, C., Sharma, S., Shukla, A.: Egyptian vulture optimization algorithm-a new nature inspired meta-heuristics for knapsack problem. In: Meesad, P., Unger, H., Boonkrong, S. (eds.) The 9th International Conference on Computing and Information Technology (IC2IT2013), pp. 227–237. Springer, Heidelberg (2013)
Sur, C., Sharma, S., Shukla, A.: Solving travelling salesman problem using egyptian vulture optimization algorithm – a new approach. In: Kłopotek, M.A., Koronacki, J., Marciniak, M., Mykowiecka, A., Wierzchoń, S.T. (eds.) IIS 2013. LNCS, vol. 7912, pp. 254–267. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38634-3_28
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
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Dixit, A., Sharma, A., Singh, A., Shukla, A. (2016). Diagnosis of Parkinson Disease Patients Using Egyptian Vulture Optimization Algorithm. In: Panigrahi, B., Suganthan, P., Das, S., Satapathy, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2015. Lecture Notes in Computer Science(), vol 9873. Springer, Cham. https://doi.org/10.1007/978-3-319-48959-9_9
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DOI: https://doi.org/10.1007/978-3-319-48959-9_9
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