eprintid: 3543
rev_number: 8
eprint_status: archive
userid: 2
dir: disk0/00/00/35/43
datestamp: 2022-09-08 23:30:05
lastmod: 2022-09-08 23:30:05
status_changed: 2022-09-08 23:30:05
type: article
metadata_visibility: show
creators_name: Rana, Arti
creators_name: Dumka, Ankur
creators_name: Singh, Rajesh
creators_name: Panda, Manoj Kumar
creators_name: Priyadarshi, Neeraj
creators_name: Twala, Bhekisipho
title: Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations
ispublished: pub
subjects: uneat_eng
divisions: uninimx_produccion_cientifica
full_text_status: public
keywords: Parkinson’s disease; machine learning; artificial neural network; logistic regression; support vector machine; classification
abstract: Parkinson’s disease (PD) is a neurodegenerative disease that affects the neural, behavioral, and physiological systems of the brain. This disease is also known as tremor. The common symptoms of this disease are a slowness of movement known as ‘bradykinesia’, loss of automatic movements, speech/writing changes, and difficulty with walking at early stages. To solve these issues and to enhance the diagnostic process of PD, machine learning (ML) algorithms have been implemented for the categorization of subjective disease and healthy controls (HC) with comparable medical appearances. To provide a far-reaching outline of data modalities and artificial intelligence techniques that have been utilized in the analysis and diagnosis of PD, we conducted a literature analysis of research papers published up until 2022. A total of 112 research papers were included in this study, with an examination of their targets, data sources and different types of datasets, ML algorithms, and associated outcomes. The results showed that ML approaches and new biomarkers have a lot of promise for being used in clinical decision-making, resulting in a more systematic and informed diagnosis of PD. In this study, some major challenges were addressed along with a future recommendation
date: 2022
publication: Diagnostics
volume: 12
number: 8
pagerange: 2003
id_number: doi:10.3390/diagnostics12082003
refereed: TRUE
issn: 2075-4418
official_url: http://doi.org/10.3390/diagnostics12082003
access: open
language: en
citation:   Artículo Materias > Ingeniería <http://repositorio.unini.edu.mx/view/subjects/uneat=5Feng.html> Universidad Internacional Iberoamericana México > Investigación > Artículos y libros <http://repositorio.unini.edu.mx/view/divisions/uninimx=5Fproduccion=5Fcientifica.html> Abierto Inglés Parkinson’s disease (PD) is a neurodegenerative disease that affects the neural, behavioral, and physiological systems of the brain. This disease is also known as tremor. The common symptoms of this disease are a slowness of movement known as ‘bradykinesia’, loss of automatic movements, speech/writing changes, and difficulty with walking at early stages. To solve these issues and to enhance the diagnostic process of PD, machine learning (ML) algorithms have been implemented for the categorization of subjective disease and healthy controls (HC) with comparable medical appearances. To provide a far-reaching outline of data modalities and artificial intelligence techniques that have been utilized in the analysis and diagnosis of PD, we conducted a literature analysis of research papers published up until 2022. A total of 112 research papers were included in this study, with an examination of their targets, data sources and different types of datasets, ML algorithms, and associated outcomes. The results showed that ML approaches and new biomarkers have a lot of promise for being used in clinical decision-making, resulting in a more systematic and informed diagnosis of PD. In this study, some major challenges were addressed along with a future recommendation metadata Rana, Arti; Dumka, Ankur; Singh, Rajesh; Panda, Manoj Kumar; Priyadarshi, Neeraj y Twala, Bhekisipho mail SIN ESPECIFICAR     <http://repositorio.unini.edu.mx/id/eprint/3543/1/diagnostics-12-02003.pdf>     (2022) Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations.  Diagnostics, 12 (8).  p. 2003.  ISSN 2075-4418     
document_url: http://repositorio.unini.edu.mx/id/eprint/3543/1/diagnostics-12-02003.pdf