Effective Identification of Features for the Diagnosis of Parkinson’s Disease using High utility Item set Mining Together with GMM
B. Mouleswararao1, Y. Srinivas2
1B.Mouleswararao, Research Scholar, Department of CSE, GITAM University, Visakhapatnam (A.P), India.
2Y.Srinivas, Professor, Department of IT, GITAM University, Visakhapatnam (A.P), India.
Manuscript received on 13 December 2018 | Revised Manuscript received on 22 December 2018 | Manuscript Published on 30 December 2018 | PP: 257-261 | Volume-8 Issue-2S, December 2018 | Retrieval Number: 100.1/ijeat.B10601282S18/18©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Disease detection is an imperative task in medical discipline. Many techniques based on image processing and data mining were employed for the early disease detection. In recent years, in spite of the latest encroachments in the science and technology, individuals experience from abundant brain disorders diseases such as Alzheimer and Parkinson. Among these diseases, Parkinson’s Disease(P.D.) is mostly influenced around the world and therefore many methodologies were emerged to combat the disease. However, as the number of symptoms prevailing to this disease is plentiful, identifying the most subjective symptom is a challenging task. This article makes an attempt to identify the most prevailing symptoms based on high utility mining together with statistical modeling, such that effective treatment can be imparted at the early stage.
Keywords: High Utility Item Set, Statistical Modeling, Parkinson’s Disease, Alzheimer Disease, Medical Imaging.
Scope of the Article: Data Mining