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Review
. 2022 Aug 19;12(8):2003.
doi: 10.3390/diagnostics12082003.

Imperative Role of Machine Learning Algorithm for Detection of Parkinson's Disease: Review, Challenges and Recommendations

Arti Rana  1 , Ankur Dumka  2 , Rajesh Singh  3   4 , Manoj Kumar Panda  5 , Neeraj Priyadarshi  6 , Bhekisipho Twala  7
Affiliations
Review

Imperative Role of Machine Learning Algorithm for Detection of Parkinson's Disease: Review, Challenges and Recommendations

Arti Rana et al. Diagnostics (Basel). .

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.

Keywords: Parkinson’s disease; artificial neural network; classification; logistic regression; machine learning; support vector machine.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A number of articles cited between 1996 and 2022.
Figure 2
Figure 2
Comparative analysis of machine learning algorithms used to diagnose Parkinson’s disease w.r.t. accuracy rate. (a) Accuracy rate of detecting Parkinson’s disease based on speech feature (2015–2020). (b) Accuracy rate of detecting Parkinson’s disease based on handwritten pattern features (2015–2020).
Figure 3
Figure 3
Symptoms of Parkinson’s disease.
Figure 4
Figure 4
Machine learning algorithm used to diagnose Parkinson’s disease.
Figure 5
Figure 5
Proposed methodology to diagnose Parkinson’s disease by [87].
Figure 6
Figure 6
Proposed methodology to diagnose Parkinson’s disease using handwriting in a spiral format by [68].
Figure 7
Figure 7
Proposed architecture of cloud and machine learning-based framework for the diagnosis of Parkinson’s disease.
See this image and copyright information in PMC

References

    1. Alzubaidi M.S., Shah U., DhiaZubaydi H., Dolaat K., Abd-Alrazaq A.A., Ahmed A., Househ M. The Role of Neural Network for the Detection of Parkinson’s disease: A Scoping Review. Healthcare. 2021;9:740. doi: 10.3390/healthcare9060740. - DOI - PMC - PubMed
    1. Maitín A.M., García-Tejedor A.J., Muñoz J.P.R. Machine Learning Approaches for Detecting Parkinson’s Disease from EEG Analysis: A Systematic Review. Appl. Sci. 2020;10:8662. doi: 10.3390/app10238662. - DOI
    1. Maserejian N., Vinikoor-Imler L., Dilley A. Estimation of the 2020 Global Population of Parkinson’s Disease (PD) [abstract] [(accessed on 7 June 2022)];Mov. Disord. 2020 35((Suppl. S1)):198. Available online: https://www.mdsabstracts.org/abstract/estimation-of-the-2020-global-popu...
    1. Van Den Eeden S.K., Tanner C.M., Bernstein A.L., Fross R.D., Leimpeter A., Bloch D.A., Nelson L.M. Incidence of Parkinson’s disease: Variation by age, gender, and race/ethnicity. Am J Epidemiol. 2003;157:1015–1022. doi: 10.1093/aje/kwg068. - DOI - PubMed
    1. Gunduz H. Deep Learning-Based Parkinson’s Disease Classification Using Vocal Feature Sets. IEEE Access. 2019;7:115540–115551. doi: 10.1109/ACCESS.2019.2936564. - DOI

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