Parkinson’s Disease Analysis and Detection using Machine Learning Technique
DOI:
https://doi.org/10.18486/ijcsnt/10.3.140Keywords:
Parkinson’s Disease, Machine Learning, Logistic Regression, Classification, DiagnosisAbstract
This chapter presents Parkinson’s disease analysis and detection using machine learning techniques. The Parkinson’s information is tested with two different models in order to determine whether model provides the most accurate categorization. Logistic Regression is used in parametric modelling to organize the Parkinson’s information that has been collected. ML Algorithms are applied to organize the preparation and test information of Parkinson’s disease. These algorithms are derived from non-parametric showing. The order is determined by combining the results of the parametric and non-parametric models with the information acquired about Parkinson’s disease. This chapter advances on research and the discussion on their importance and efficiency in the efficient treatment and further diagnosis.
References
Elbaz A, Bower JH, Peterson BJ et al. Endurance study of Parkinson disease in Olmsted County, Minnesota. Archives of Neurology 2003; 60: 91–96. DOI: https://doi.org/10.1001/archneur.60.1.91
Parkinson J. An exposition on the shaking palsy. 1817. Journal of Neuropsychiatry and Clinical Neurosciences 2002; 14: 223–236. DOI: https://doi.org/10.1176/jnp.14.2.223
Tanner CM, Ross GW, Jewell SA et al. Occupation and hazard of parkinsonism: a multicenter case-control study. Archives of Neurology 2009; 66: 1106–1113. DOI: https://doi.org/10.1001/archneurol.2009.195
Marras C and Tanner C. The study of disease transmission of Parkinson’s disease: developmental disorders. In: Watts RL and Koller WC, eds. Neurologic Principles and Practice. 2nd ed. New York: The McGraw-Hill Companies, 2004, p. 177.
US Census Bureau. US birth projections by age, sex, race, and Hispanic origin: 2000–2050. 2012. Accessed April 7, 2012.
Dauer W and Przedborski S. Parkinson’s disease: mechanisms and models. Neuron 2003; 39: 889–909. DOI: https://doi.org/10.1016/S0896-6273(03)00568-3
Alonso JB, de Leon J, Alonso I et al. Programmed location of pathologies in the voice by HOS-based boundaries. EURASIP Journal on Applied Signal Processing 2001; 14: 275–284. DOI: https://doi.org/10.1155/S1110865701000336
Cnockaert L, Schoentgen J, Auzou P et al. Low-recurrence vocal balances in vowels created by parkinsonian subjects. Speech Communication 2008; 50: 288–300. DOI: https://doi.org/10.1016/j.specom.2007.10.003
Revett K, Gorunescu F and Mohamed Salem AB. Highlight selection in Parkinson’s disease: a rough sets approach. In Proceedings of the International Multi-Conference on Computer Science and Information Technology, vol. 4, Margowo, Poland.
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Copyright (c) 2021 Padmanjali A A Hagargi, Rajshekar G, Masrat Begum, Manikrao M
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