ISSN 2394-5125
 

Research Article 


IDENTIFICATION OF PARKINSONíS DISEASE USING MACHINE LEARNING

Dr.M.J.Carmel Mary Belinda, Rupavathy.N.

Abstract
Parkinsonís disease, the second most common neurological disorder that causes severe impairment, compromises the quality of life and is incurable. Approximately, 90 percent of people affected by Parkinsonís have speech problems. Exposure to PD is divided into two types i.e. Non-motor and motor signs. Several people know the carís symptoms as they can be seen in people. This expression is also called Cardinal Symbols, this includes slowing down, slowing down (bradykinesia), post-traumatic instability and fitness. Medical databases contain big data in the form of documents, numbers and images of minerals. Big Data can provide valuable information after processing that can be obtained through in-depth analysis and data efficiency by decision-makers. Data mining is the process of selecting, extracting, and displaying unknown hidden layers in big data. The machine learning algorithm can be used for early diagnosis to increase the life expectancy of the elderly and for an improved lifestyle with Parkinsonís. Thanks to the recent introduction of technology and the proliferation of sound-collecting devices in everyday life, reliable models that can turn this audio data into a diagnostic tool for health- care professionals are likely to provide a cheaper and more accurate diagnosis. Parkinsonís disease can damage the human body at an early age, due to the unhealthy human body, expanding rapidly.

Key words: Parkinsonís Disease, Machine Learning, Principle XG Boost Algorithm


 
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How to Cite this Article
Pubmed Style

Dr.M.J.Carmel Mary Belinda, Rupavathy.N. IDENTIFICATION OF PARKINSONíS DISEASE USING MACHINE LEARNING. JCR. 2020; 7(19): 9745-9755. doi:10.31838/jcr.07.19.1078


Web Style

Dr.M.J.Carmel Mary Belinda, Rupavathy.N. IDENTIFICATION OF PARKINSONíS DISEASE USING MACHINE LEARNING. http://www.jcreview.com/?mno=11959 [Access: August 17, 2021]. doi:10.31838/jcr.07.19.1078


AMA (American Medical Association) Style

Dr.M.J.Carmel Mary Belinda, Rupavathy.N. IDENTIFICATION OF PARKINSONíS DISEASE USING MACHINE LEARNING. JCR. 2020; 7(19): 9745-9755. doi:10.31838/jcr.07.19.1078



Vancouver/ICMJE Style

Dr.M.J.Carmel Mary Belinda, Rupavathy.N. IDENTIFICATION OF PARKINSONíS DISEASE USING MACHINE LEARNING. JCR. (2020), [cited August 17, 2021]; 7(19): 9745-9755. doi:10.31838/jcr.07.19.1078



Harvard Style

Dr.M.J.Carmel Mary Belinda, Rupavathy.N (2020) IDENTIFICATION OF PARKINSONíS DISEASE USING MACHINE LEARNING. JCR, 7 (19), 9745-9755. doi:10.31838/jcr.07.19.1078



Turabian Style

Dr.M.J.Carmel Mary Belinda, Rupavathy.N. 2020. IDENTIFICATION OF PARKINSONíS DISEASE USING MACHINE LEARNING. Journal of Critical Reviews, 7 (19), 9745-9755. doi:10.31838/jcr.07.19.1078



Chicago Style

Dr.M.J.Carmel Mary Belinda, Rupavathy.N. "IDENTIFICATION OF PARKINSONíS DISEASE USING MACHINE LEARNING." Journal of Critical Reviews 7 (2020), 9745-9755. doi:10.31838/jcr.07.19.1078



MLA (The Modern Language Association) Style

Dr.M.J.Carmel Mary Belinda, Rupavathy.N. "IDENTIFICATION OF PARKINSONíS DISEASE USING MACHINE LEARNING." Journal of Critical Reviews 7.19 (2020), 9745-9755. Print. doi:10.31838/jcr.07.19.1078



APA (American Psychological Association) Style

Dr.M.J.Carmel Mary Belinda, Rupavathy.N (2020) IDENTIFICATION OF PARKINSONíS DISEASE USING MACHINE LEARNING. Journal of Critical Reviews, 7 (19), 9745-9755. doi:10.31838/jcr.07.19.1078