UGC Approved Journal no 63975(19)

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Published in:

Volume 9 Issue 5
May-2022
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2205862


Registration ID:
402991

Page Number

h482-h487

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Title

EARLY DIAGNOSIS OF PARKINSON’S DISEASE USING MACHINE LEARNING ALGORITHM

Abstract

Medical observations and assessment of scientific symptoms and symptoms, which encompass the identification of an entire lot of motor symptoms and symptoms, are often used to diagnose Parkinson's sickness (PD). Traditional diagnostic strategies, on the alternative hand, may be at risk of subjectivity because of the truth they rely on the assessment of motions which is probably every now and then subtle to human sight and as an end result tough to outline, possibly fundamental to misdiagnosis. Meanwhile, early non-motor symptoms and symptoms of Parkinson's sickness can be minor and be due to an entire lot of various illnesses. As a give-up end result, the symptoms and symptoms are regularly missed, making early PD diagnosis tough. Machine studying algorithms for the method of Parkinson's disease and healthy volunteers or clients with comparable scientific suggests were observed to resolve the one’s issues and algorithms for Parkinson's disease treatment and healthy volunteers or clients with strategies of PD (e.g., movement issues or extraordinary Parkinsonian syndromes). We conducted a systematic literature review of papers published up to February 14, 2020, by using PubMed and IEEE Xplore databases, to provide an in-depth evaluation of information modalities and machine learning strategies used in the diagnosis and differential diagnosis of Parkinson's disease. This assessment began to look at the objectives, reasserts of information, types of information, machine study strategies, and associated outcomes of the entire 209 research, which was then extracted for relevant information and introduced in this assessment. These consequences show that machine studying strategies and novel biomarkers have some of the functionality for being applied in scientific choice-making, resulting in a greater systematic and informed diagnosis of Parkinson's sickness. Keywords: Clinical symptoms and symptoms, motor, and non-motor symptoms and symptoms, XGBoost Algorithm.

Key Words

Parkinson's disease, machine learning, deep learning, diagnosis, differential diagnosis

Cite This Article

"EARLY DIAGNOSIS OF PARKINSON’S DISEASE USING MACHINE LEARNING ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.h482-h487, May-2022, Available :http://www.jetir.org/papers/JETIR2205862.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"EARLY DIAGNOSIS OF PARKINSON’S DISEASE USING MACHINE LEARNING ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. pph482-h487, May-2022, Available at : http://www.jetir.org/papers/JETIR2205862.pdf

Publication Details

Published Paper ID: JETIR2205862
Registration ID: 402991
Published In: Volume 9 | Issue 5 | Year May-2022
DOI (Digital Object Identifier):
Page No: h482-h487
Country: Hyderabad, Telangana, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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