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Rebecca Dyanne Wooten
Keynote: J Neurol Clin Neurosci
There are many key factors that contribute to the onset of Parkinson’s disease. One contributing factor is heredity. In an article I contributed to entitled On Heredity Factors of Parkinson’s
Disease: A Parametric and Bayesian Analysis, we modeled the chances or likelihood of an individual developed Parkinson’s disease and using real world data in conjunction with Maximum Likelihood Estimation and Bayesian Analysis, it was shown that age, family history, and gender are contributing factors. However, and moreover, it was shown that environmental factors and head trauma are also significantly contributing factors. This leads to assessments of risk factors and the possibility of early detection. Two key priorities are early detection and treatment. Suggested risk factors, in addition to gender include age, race and ethnicity. We discuss the issues that arise when as a data scientist, the analysis of the data reveal incomplete and/or dirty data.
Rebecca Dyanne Wooten is a Researcher and Data Analyst who has been being treated for Parkinson’s disease since 2012. She has, since the start of her treatment begun seeking data to help her better understand what caused the early onset of Parkinson’s and to learn how to find balance and manager her condition. She has been a reviewer for the Michael J. Fox Foundation, has worked with an intern (now working at Tampa General Hospital) and contributed to articles on the topic of Parkinson’s disease. Her primary area in theoretical statistics is regression and she has developed new methods of regression that model codependent relationships extending standard regression to implicit regression which includes non-response analysis and rotational analysis.
E-mail: [email protected]