IJSDR
IJSDR
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

Issue: March 2024

Volume 9 | Issue 3

Impact factor: 8.15

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Paper Title: A Study of Feature Selection with Machine Learning based Decision Making Models for Parkinson`s Disease Classification
Authors Name: Mrs. N Navaneetha , Dr. T. Suresh , Dr.V.Sathiyasuntharam
Unique Id: IJSDR2210137
Published In: Volume 7 Issue 10, October-2022
Abstract: Parkinson’s disease (PD) refers to a neurodegenerative disease that affects many persons globally and cannot be cured basically. It is highly essential to diagnosePD in the early phases so that an individual could live a healthy life for a long time. The severe stages of PD were extremely risky because the victims get stiffness, which leads to walking or standing inability. Previous researchers had a focus on detecting PD efficiently by utilizing writing exams and voice and speech exams. In recent times, count of features and count of instances that make data louder have enhanced data. Nowadays, automatic detection of initialPD on feature data sets seems to be difficult tasks in healthcare sector. Most of the features in such data were unusable or involves problems such as noise, which affect the learning procedure and rises the computational burden. Feature selection becomes one of the most significant and broadly utilized methods for data pre-processing. It can be typically utilized for finding the optimum subset of features, eliminate redundant and irrelevant features, reduce computing complexity, and enhance the performance of classification recognition. Existing works are mainly based on feature selection with machine learning (ML) based PD classification models. Since numerous works existed in the literature, this paper focuses on the performance validation of different PD classification models. The recently developed feature selection with data classification models using ML and deep learning (DL) for PD classification is examined. In addition, the performance analysis of different PD classification models is made and the results are inspected interms of different measures.
Keywords: Parkinson’s disease; Machine learning; Data mining; Feature selection; Deep learning; Data classification
Cite Article: "A Study of Feature Selection with Machine Learning based Decision Making Models for Parkinson`s Disease Classification", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 10, page no.806 - 814, October-2022, Available :http://www.ijsdr.org/papers/IJSDR2210137.pdf
Downloads: 000336256
Publication Details: Published Paper ID: IJSDR2210137
Registration ID:202271
Published In: Volume 7 Issue 10, October-2022
DOI (Digital Object Identifier):
Page No: 806 - 814
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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