%0 Journal Article %1 noauthororeditor %A Revathi, %D 2023 %J International Journal of Innovative Research in Information Security %K analysing machine performance support vector %N 2 %P 50-54 %R 10.26562/ijiris.2023.v0902.08 %T Analysing the Performance of Support Vector Machine Algorithm in Predicting Parkinson’s Disease %U http://www.ijiris.com/volumes/Vol09/iss-02/08.MYIS10096.pdf %V 09 %X Parkinson’s disease (PD) is a neurodegenerative measure disease where the symptoms steadily develop start with an insignificant a slight shaking movement in one hand and a feeling of stiffness in the body and it became worse over time. Dopamine is most notably involved in helping us feel preference as part of the brain's reward system. It acts as a message between the parts of the brain and nervous system that support control and co-ordinate body movements. As dopamine generally neurons in the parts begin to experience difficulty in speaking, writing, walking or completing another simple task. Virtually, 90% pretentious people with Parkinson have tongue disorders. It affects over 6 million people worldwide. At present there is no conclusive result for this disease by non-specialist clinicians, particularly in the early stage of the disease where identification of the symptoms is very difficult in its earlier stages. The future predictive analytics outline is a combination of K-means clustering and Decision Tree which is used to improvement visions from patients. By using machine learning methods, the problem can be explained with negligible error rate. The investigated approach serves as a guide in determining the diagnosis and in planning the appropriate by a loss of nerve cells in the part of the brain.

@article{noauthororeditor, abstract = {Parkinson’s disease (PD) is a neurodegenerative measure disease where the symptoms steadily develop start with an insignificant a slight shaking movement in one hand and a feeling of stiffness in the body and it became worse over time. Dopamine is most notably involved in helping us feel preference as part of the brain's reward system. It acts as a message between the parts of the brain and nervous system that support control and co-ordinate body movements. As dopamine generally neurons in the parts begin to experience difficulty in speaking, writing, walking or completing another simple task. Virtually, 90% pretentious people with Parkinson have tongue disorders. It affects over 6 million people worldwide. At present there is no conclusive result for this disease by non-specialist clinicians, particularly in the early stage of the disease where identification of the symptoms is very difficult in its earlier stages. The future predictive analytics outline is a combination of K-means clustering and Decision Tree which is used to improvement visions from patients. By using machine learning methods, the problem can be explained with negligible error rate. The investigated approach serves as a guide in determining the diagnosis and in planning the appropriate by a loss of nerve cells in the part of the brain.}, added-at = {2023-06-06T07:01:16.000+0200}, author = {Revathi}, biburl = {https://www.bibsonomy.org/bibtex/2f52b43a55517da6a9e1dbc84d72bf4c1/ijiris}, description = {In future work, we plan to increase our subject pool and utilize optimal features election strategies under MIL frameworks for developing robust person-specific models. These techniques can potentially be adapted to various other physiological sensing and monitoring applications as well. A machine learning approach that works to identify mitochondrial impairments, a potential mechanism underlying the pathogenesis of Parkinson disease (PD), accurately.}, doi = {10.26562/ijiris.2023.v0902.08}, interhash = {2375905a216b2ccccc3f39083a8c48a3}, intrahash = {f52b43a55517da6a9e1dbc84d72bf4c1}, issn = {2349-7017}, journal = {International Journal of Innovative Research in Information Security}, keywords = {analysing machine performance support vector}, language = {English}, month = {May 2023}, note = {[1]. Muhlenbach, F. and Rakotomalala, R. (2015), “Discretization of Continuous Attributes,” In: Wang, J., Ed., Encyclopedia of Data Warehousing and Mining, Idea Group Reference, 397-402. https://doi.org/10.4018/978-1-59140-557-3.ch076 [2]. [M. Liu, D. Zhang, and D. Shen, “Relationship induced multi- 52 template learning for diagnosis of alzheimer’s disease and mild cognitive impairment,” IEEE transactions on medical imaging, vol. 35, no. 6, p. 1463—1474, (2016). https://doi.org/10.1109/tmi.2016.2515021 [3]. Abdullah Caliskan, Hasan Badem, A. Baştürk, M.E. Yüksel, “Diagnosis Of The Parkinson Disease By Using Deep Neural Network Classifier”, Journal Of Electrical And Electronics Engineering, (2017):3311-3318 [4]. M. Abdar and M. Zomorodi-Moghadam, “Impact of Patients’ Gender on Parkinson’s disease using Classification Algorithms” Journal of AI and Data Mining, vol. 6, (2018). [5]. Sleigh, J.W.; Olofsen, E.; Dahan, A.; de Goede, J.; Steyn-Ross, A. Entropies of the EEG: The effects of general anaesthesia. In Proceedings of the 5th International Conference on Memory, Awareness and Consciousness, New York, NY, USA, 1–3 June (2019) [6]. Tsuda, M., Asano, S., Kato, Y., Murai, K., and Miyazaki, M. (2020). Differential diagnosis of multiple system atrophy with predominant parkinsonism and Parkinson's disease using neural networks. J. Neurol. Sci. 401, 19–26. https://doi.org/10.1016/j.jns.2019.04.014 [7]. Tysnes, O.-B., and Storstein, A. (2021). Epidemiology of Parkinson's disease. J. Neural Trans. 124, 901–905. https://doi:10.1007/s00702-017-1686-y [8]. Parkinson J. An Essay on the Shaking Palsy. London: Sherwood, Neely, and Jones; 1817. pp. 1–16. [Google Scholar] [9]. Twelves D, Perkins KS, Counsell C. Systematic review of incidence studies of Parkinson’s disease. Mov Disord. 2003;18:19–31. [PubMed] [Google Scholar] [10]. National Institute for Health and Care Excellence (NICE) Parkinson’s disease: diagnosis and management in primary and secondary care. NICE clinical guidelines 35. Jun, 2006. Available https://doi.org/10.1136/heartjnl-2022-321095 http://www.nice.org.uk/guidance/cg35/resources/guidance-parkinsons-disease-pdf. Accessed April 28, 2015. [11]. Schrag A, Horsfall L, Walters K, et al. Prediagnostic presentations of Parkinson’s disease in primary care: a case-control study. Lancet Neurol. 2015; [PubMed] [Google Scholar] https://doi.org/10.1016/s1474-4422(14)70287-x [12]. Parkinson’s Disease Foundation Statistics on Parkinson’s. Available at: www.pdf.org.en/parkinson_statistics Accessed June 19, 2014. [13]. Driver JA, Logroscino G, Gaziano JM, et al. Incidence and remaining lifetime risk of Parkinson disease in advanced age. Neurology. 2009;72:32–38. [PMC free article] [PubMed] [Google Scholar] https://doi.org/10.1212/01.wnl.0000341769.50075.bb [14]. De Lau LM, Breteler MM. Epidemiology of Parkinson’s disease. Lancet Neurol. 2006;5:525–535. [PubMed] [Google Scholar] [15]. Miller IN, Cronin-Golomb A. Gender differences in Parkinson’s disease: clinical characteristics and cognition. Mov Disord. 2010;25:2695–2703. [PMC free article] [PubMed] [Google Scholar] https://doi.org/10.1002/mds.23388 [16]. Rumayor MA, Arrieta O, Sotelo J, et al. Female gender but not cigarette smoking delays the onset of Parkinson’s disease. Clin Neurol Neurosurg. 2009;[PubMed] [Google Scholar] https://doi.org/10.1016/j.clineuro.2009.07.012 [17]. Gómez-Esteban JC, Zarranz JJ, Lezcano E, et al. Influence of motor symptoms upon the quality of life of patients with Parkinson’s disease. Eur Neurol. 2007;57:161–165. [PubMed] [Google Scholar] https://doi.org/10.1159/000098468 [18]. Pennington S, Snell K, Lee M, et al. The cause of death in idiopathic Parkinson’s disease. Parkinsonism Relat Disord. 2010;16:434–437. [PubMed] [Google Scholar] https://doi.org/10.1016/j.parkreldis.2010.04.010 [19]. Jankovic J, Poewe W. Therapies in Parkinson’s disease. Curr Opin Neurol. 2012; [PubMed] [Google Scholar] [20]. Smith Y, Wichmann T, Factor SA, DeLong MR. Parkinson’s disease therapeutics: new developments and challenges since the introduction of levodopa. Neuropsycho pharmacology. 2012;[PMC free article] [PubMed] [Google Scholar] https://doi.org/10.1038/npp.2011.212 [21]. Simonson W, Hauser RA, Schapira AHV. Role of the pharmacist in the effective management of wearing-off in Parkinson’s disease. Ann Pharmacother.2007; [PubMed] [Google Scholar] https://doi.org/10.1345/aph.1k348 [22]. Van der Marck MA, Bloem BR, Borm GF, et al. Effectiveness of multidisciplinary care for Parkinson’s disease: a randomized, controlled trial. Mov Disord. 2013;[PubMed] [Google Scholar] https://doi.org/10.1002/mds.25194 [23]. Zhou C, Huang Y, Przedborski S. Oxidative stress in Parkinson’s disease: a mechanism of pathogenic and therapeutic significance. Ann NY Acad Sci. 2008;1147:93–104. [PMC free article] [PubMed] [Google Scholar] https://doi.org/10.1196/annals.1427.023 [24]. Logroscino G. The role of early-life environmental risk factors in Parkinson disease: what is the evidence? Environ Health Perspect. 2005; [PMC free article] [PubMed] [Google Scholar] https://doi.org/10.1289/ehp.7573 [25]. Spatola M, Wider C. Genetics of Parkinson’s disease: the yield. Parkinsonism Relat Disord. 2014;20(suppl 1):S35–S38. [PubMed] [Google Scholar] https://doi.org/10.1016/s1353-8020(13)70011-7 [26]. Singleton AB, Farer MJ, Bonifati V. The genetics of Parkinson’s disease: progress and therapeutic implications. Mov Disord. 2013;28:14–23. [PMC free article] [Pu bMed] [Google Scholar] https://doi.org/10.1002/mds.25249 [27]. Santiago JA, Scherzer CR, Potashkin JA. Network analysis identifies SOD2 mRNA as a potential biomarker for Parkinson’s disease. PLoS One. 2014; [PMC free article] [PubMed] [Google Scholar] https://doi.org/10.1371/journal.pone.0109042 [28]. Liu R, Guo X, Park Y, et al. Caffeine intake, smoking, and risk of Parkinson disease in men and women. Am J Epidemiol. 2012; [PMC free article] [PubMed] [Google Scholar] https://doi.org/10.1093/aje/kwr451 [29]. Benmoyal-Segal L, Soreq H. Gene–environment interactions in sporadic Parkinson’s disease. J Neurochem. 2006;97:1740–1755. [PubMed] [Google Scholar] https://doi.org/10.1111/j.1471-4159.2006.03937.x [30]. Van der Merwe C, Haylett W, Harvey J, et al. Factors influencing the development of early- or late-onset Parkinson’s disease in a cohort of South African patients. S Afr Med J. 2012;102:848–851. [PubMed] [Google Scholar] https://doi.org/10.7196/samj.5879 [31]. Wang G, Pan J, Chen SD. Kinases and kinase signaling pathways: potential therapeutic targets in Parkinson’s disease. Prog Neurobiol. 2012; [PubMed] [Google Scholar] https://doi.org/10.1016/j.pneurobio.2012.06.003 [32]. Gilgun-Sherki Y, Djaldetti R, Melamed E, Offen D. Polymorphism in candidate genes: implications for the risk and treatment of idiopathic Parkinson’s disease. Pharmacogenomics J. 2004;4:291–306. [PubMed] [Google Scholar] https://doi.org/10.1038/sj.tpj.6500260 [33]. Agúndez JA, García-Martín E, Alonso-Navarro H, et al. Anti-Parkinson’s disease drugs and pharmacogenetic considerations. Expert Opin Drug Metab Toxicol. 2013;9:859–874. [PubMed] [Google Scholar] https://doi.org/10.1517/17425255.2013.789018 [34]. Marras C, Goldman SM. Genetics meets environment: evaluating gene–environment interactions in neurologic diseases. Semin Neurol. 2011;31:553–561. [PubMed] [Google Scholar] https://doi.org/10.1055/s-0031-1299793 [35]. Soreq L, Ben-Shaul Y, Israel Z, et al. Meta-analysis of genetic and environmental Parkinson’s disease models reveals a common role of mitochondrial protection pathways. Neurobiol Dis. 2012;45:1018–1030. [PubMed] [Google Scholar] https://doi.org/10.1016/j.nbd.2011.12.021 [36]. Curtin K, Fleckenstein AE, Robison RJ, et al. Methamphetamine/amphetamine abuse and risk of Parkinson’s disease in Utah: a population-based assessment. Drug Alcohol Depend. 2015;146:30–38. [PMC free article] [PubMed] [Google Scholar] https://doi.org/10.1016/j.drugalcdep.2014.10.027 [37]. Chen JJ, Swope DM. Parkinson’s disease. In: DiPiro JT, Talbert RL, Yee GC, et al., editors. Pharmacotherapy: A Pathophysiologic Approach. 9th ed. New York, New York: McGraw-Hill; 2014. [Google Scholar] [38]. Chade AR, Kasten M, Tanner CM. Nongenetic causes of Parkinson’s disease. J Neural Transm Suppl. 2006;70:147–151. [PubMed] [Google Scholar] https://doi.org/10.1007/978-3-211-45295-0_23}, number = 2, pages = {50-54}, timestamp = {2023-06-06T07:01:16.000+0200}, title = {Analysing the Performance of Support Vector Machine Algorithm in Predicting Parkinson’s Disease}, url = {http://www.ijiris.com/volumes/Vol09/iss-02/08.MYIS10096.pdf}, volume = 09, year = 2023 }