• Home
  • Submit Paper
  • Check Paper Status
  • Download Certificate/Paper
  • FAQs
  • Contact Us
Email: ijraset@gmail.com
IJRASET Logo
Journal Statistics & Approval Details
Recent Published Paper
Our Author's Feedback
 •  ISRA Impact Factor 7.894       •  SJIF Impact Factor: 7.538       •  Hard Copy of Certificates to All Authors       •  DOI by Crossref for all Published Papers       •  Soft Copy of Certificates- Within 04 Hours       •  Authors helpline No: +91-8813907089(Whatsapp)       •  No Publication Fee for Paper Submission       •  Hard Copy of Certificates to all Authors       •  UGC Approved Journal: IJRASET- Click here to Check     
  • About Us
    • About Us
    • Aim & Scope
  • Editorial Board
  • Impact Factor
  • Call For Papers
    • Submit Paper Online
    • Current Issue
    • Special Issue
  • For Authors
    • Instructions for Authors
    • Submit Paper
    • Download Certificates
    • Check Paper Status
    • Paper Format
    • Copyright Form
    • Membership
    • Peer Review
  • Past Issue
    • Monthly Issue
    • Special Issue
  • Pay Fee
    • Indian Authors
    • International Authors
  • Topics
ISSN: 2321-9653
Estd : 2013
IJRASET - Logo
  • Home
  • About Us
    • About Us
    • Aim & Scope
  • Editorial Board
  • Impact Factor
  • Call For Papers
    • Submit Paper Online
    • Current Issue
    • Special Issue
  • For Authors
    • Instructions for Authors
    • Submit Paper
    • Download Certificates
    • Check Paper Status
    • Paper Format
    • Copyright Form
    • Membership
    • Peer Review
  • Past Issue
    • Monthly Issue
    • Special Issue
  • Pay Fee
    • Indian Authors
    • International Authors
  • Topics

Ijraset Journal For Research in Applied Science and Engineering Technology

  • Home / Ijraset
  • On This Page
  • Abstract
  • Introduction
  • Conclusion
  • References
  • Copyright

Parkinsons Disease Detection Using Machine Learning Algorithm: A Review of Literature

Authors: Md. Toukir Ahmed, Md. Nazrul Islam Mondal, Mohammed Sowket Ali, Md. Moshaheb Hossain, Mahabuba

DOI Link: https://doi.org/10.22214/ijraset.2022.44340

Certificate: View Certificate

Abstract

Parkinson\'s disease (PD), or simply Parkinson\'s is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. A quantitative analysis of handwriting samples would be valuable as it could supplement and support clinical assessments, help monitor micrographic, and link it to PD. Such an analysis would be especially useful if it could detect subtle yet relevant changes in handwriting morphology, thus enhancing solution of the detection procedure. We can find several works that attempt at dealing with this problem out there, most of them make use of datasets composed by a few subjects only. In this study, we conducted a literature review of studies that applied machine learning models to movement data to diagnose PD published in 2019, using the PubMed and IEEE Xplore databases, to provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of Parkinson\'s disease. In this research, we investigated their goals, data sources, data kinds, machine learning methodologies, and associated outcomes.

Introduction

I. INTRODUCTION

Parkinson's disease (PD) is a degenerative neurological illness that is persistent. The primary etiology of Parkinson's disease is uncertain. However, it has been shown that a mix of environmental and genetic variables play a crucial role in the development of Parkinson's disease [1]. It is a well-known fact that around one million individuals in the United States suffer from Parkinson's disease, while approximately five million people globally suffer from Parkinson's disease. As a result, it is critical to forecast Parkinson's disease in its early stages so that therapy may be planned ahead of time. Non-motor and motor symptoms are the two forms of Parkinson's disease symptoms. Many individuals are aware of motor symptoms since they can be seen with the naked eye. Resting tremor, slowness of movement (bradykinesia), postural instability (balance issues), and stiffness are examples of cardinal symptoms [2]. People are generally familiar with Parkinson's disease's motor symptoms, but an increasing amount of research is being done to predict Parkinson's disease from non-motor symptoms that precede the motor ones. If an accurate and timely prognosis is achievable, a patient can receive appropriate therapy at the appropriate time Nonmotor symptoms taken into account include Rapid Eye Movement (REM), Sleep Behaviour Disorder (RBD), and olfactory loss Developing machine learning models that can aid in illness prediction can play a critical role in early detection. In this work, we used the PubMed and IEEE Xplore databases to perform a literature analysis of papers that applied machine learning models to movement data to diagnose PD published in 2019 & 2018 to offer a thorough overview of data modalities and machine learning algorithms used in the diagnosis and differential diagnosis of Parkinson's disease. We evaluated their aims, data sources, data types, machine learning approaches, and associated outcomes in this study.

II. JUSTIFICATION OF THE STUDY

Parkinson's disease (PD) is a neurological illness that affects a person's movements, and may cause tremors, slowness of movement, muscle stiffness and imbalance as well as changes in speech and writing skills [3]. One of the most challenging tasks when dealing with PD diagnosis is whether to use visual and/or signal based information from patient exams. As aforementioned, previous works have used high-end image technology (MRI) for such purposes, but being expensive and may be invasive enough to the patient as well. Additionally, most signal-based datasets for PD recognition are small and biased, which may not reject the real world. In order to overcome such shortcomings, we need to develop a new dataset composed of images. Proper research can enhance the performance of the above-mentioned problem domains. For these reason we need to to measure and compare the performance with the previous studies.

III. OBJECTIVES WITH SPECIFIC AIMS

In this study, we will use the PubMed and IEEE Xplore databases to conduct a literature review of papers that applied machine learning models to movement data to diagnose PD published in 2019, in order to provide a comprehensive overview of data modalities and machine learning algorithms used in the diagnosis and differential diagnosis of Parkinson's disease.In this study, we will assesse their objectives, data sources, data kinds, machine learning methodologies, and associated outcomes.

TABLE I: SOURCE OF DATA & PERFORMANCE METRIC OF THE INCLUDED STUDIES

Source of data

Performance metric

independent recruitment of human participants

PPMI database

PhysioNet

mPower database

Others

(1 PPMI + Sheffield Teaching Hospitals NHS

Foundation Trust;

1 PPMI + Seoul National University Hospital cohort;

1 UCI + collected from participants

Accuracy

Sensitivity (recall)

Specificity (TNR)

AUC

MCC

Precision (PPV)

NPV

F1 score

Others

(7 kappa; 4 error rate; 3 EER; 1

MSE; 1 LOR; 1 confusion matrix; 1

cross validation score; 1 YI; 1 FPR; 1

FNR; 1 G-mean; 1 PE; 5

combination of metrics)

 

TABLE II: RELATED WORKS

Type of

Diagnosis

,Source of data

Objectives

Machine learning

method(s)

 

Outcomes

Year

References

Diagnosis and

differential

diagnosis,Collected from

participants

Classification of PD,

HC and other

neurological stance

disorders

Ensemble method of 7

models (logistic regression,

KNN, shallow and deep

ANNs, SVM, random

forest, extra-randomized

trees) with 90% training

and 10% testing data in

stratified k-fold

cross-validation

8-class classification

accuracy = 82.7%

2019

[4]

Diagnosis,Collected from

participants,Collected from

participants

Classification of PD

from HC

SVM (linear, quadratic,

cubic, Gaussian kernels),

ANN, with 5-fold

cross-validation

Classification with ANN:

Accuracy = 89.4%

Sensitivity = 87.0%

Specificity = 91.8%

Severity assessment with

ANN:

Accuracy = 95.0%

sensitivity = 90.0%

Specificity = 99.0%

2019

[5]

Diagnosis,Collected from

participants

Classification of PD,

HC and PD, HC, IH

SVM, random forest, naïve

Bayes with 10-fold cross

validation

Random forest:

HC vs. PD:

Accuracy = 0.950

F-measure = 0.947

HC + IH vs. PD:

Accuracy = 0.917

F-measure = 0.912

HC vs. IH vs. PD:

Accuracy = 0.789

F-measure = 0.796

2019

[6]

Diagnosis,Collected from

participants

Classification of PD

from HC

Deep-MIL-CNN with LOSO

or RkF

With LOSO:

Precision = 0.987

Sensitivity = 0.9

specificity = 0.993

F1-score = 0.943

With RkF:

Precision = 0.955

Sensitivity = 0.828

Specificity = 0.979

F1-score = 0.897

2019

[7]

Diagnosis,Collected from

participants

Classification of PD

from HC

LSTM, CNN-1D,

CNN-LSTM with 5-fold

cross-validation and a

training-test ratio of 90:10

CNN-LSTM: Accuracy = 83.1%

Precision = 83.5%

Recall = 83.4%

F1-score = 81%

Kappa = 64%

2019

[8]

Diagnosis,Collected from

participants

Classification of PD

from HC

Naïve Bayes, KNN, SVM

with leave-one-out cross

validation

SVM: Accuracy = 95%

Precision = 0.951

AUC = 0.950

2019

[9]

Diagnosis and

differential

diagnosis,Collected from

participants

Classification of PD,

HC and IH

SVM-polynomial, random

forest with 5-fold cross

validation

HC vs. PD, random forest: Precision = 1.000

Recall = 1.000

Specificity = 1.000

Accuracy = 1.000

F-measure = 1.000

Multiclass classification

(HC vs. IH vs. PD),

random forest:

Precision = 0.930

Recall = 0.911

Specificity = 0.956

Accuracy = 0.911

F-measure = 0.920

2019

[10]

Diagnosis,PhysioNet

Classification of PD

from HC and assess

the severity of PD

1D-CNN, 2D-CNN, LSTM,

decision tree, logistic

regression, SVM, MLP

2D-CNN and LSTM

accuracy = 96.0%

2019

[11]

Diagnosis,PhysioNet

Classification of PD

from HC

SVM-Gaussian with 3- or

5-fold cross validation

Accuracy = 100%,

88.88%, and 100% in

three test groups

2019

[12]

Diagnosis,PhysioNet

Classification of PD

from HC

SVM-linear, KNN, naïve

Bayes, LDA, decision tree

with leave-one-out cross

validation

SVM, KNN and decision

tree accuracy = 96.8%

2019

[13]

Diagnosis,PhysioNet

Classification of PD

from HC

KNN, CART, decision tree,

random forest, naïve

Bayes, SVM-polynomial,

SVM-linear, K-means,

GMM with leave-one-out

cross validation

SVM:

Accuracy = 90.32%

Precision = 90.55%

Recall = 90.21%

F-measure = 90.38%

2019

[14]

Diagnosis,PhysioNet

Classification of PD

from HC

DCALSTM with stratified

5-fold cross validation

Sensitivity = 99.10%

Specificity = 99.01%

Accuracy = 99.07%

2019

[15]

Differential

diagnosis, ,Collected from

participants

Classification of PD

from MSA

SVM with

leave-one-out-cross

validation

MSA vs. PD: 2019 Accuracy = 0.79

Sensitivity = 0.71

Specificity = 0.86

MSA vs. HC:

Accuracy = 0.79

Sensitivity = 0.84

Specificity = 0.74

MSA vs. subsample of PD:

Accuracy = 0.84

Sensitivity = 0.77

Specificity = 0.90

2019

[16]

Differential

diagnosis,Collected from

participants

Classification of PD

from MSA

SVM with

leave-one-out-cross

validation

Accuracy = 77.17% Sensitivity = 83.33%

Specificity = 74.19%

2019

[17]

Diagnosis,Collected from

participants

Classification of PD

from HC

CNN with 85 subjects for

training and 9 for testing

Training accuracy = 95.24%

Testing accuracy = 88.88%

2019

[18]

Diagnosis and

differential

diagnosis,Collected from

participants

Classification of PD,

PSP, MSA-P and HC

CNN with train-validation

ratio of 85:15

PD:

Sensitivity = 94.4%

Specificity = 97.8%

Accuracy = 96.8%

AUC = 0.995

PSP:

Sensitivity = 84.6%

Specificity = 96.0%

Accuracy = 93.7%

AUC = 0.982

MSA-P:

Sensitivity = 77.8%

Specificity = 98.1%

Accuracy = 95.2%

AUC = 0.990

HC:

Sensitivity = 100.0%

Specificity = 97.5%

Accuracy = 98.4%

AUC = 1.000

2019

[19]

Diagnosis,Collected from

participants

Classification of PD

from HC

Boosted logistic regression

with nested

cross-validation

Accuracy = 76.2% Sensitivity = 81%

Specificity = 72.7%

2019

[20]

Diagnosis and

differential

diagnosis,Collected from

participants

Classification of PD,

APS (MSA, PSP) and

HC

CNN-DL, CR-ML, RA-ML

with 5-fold cross-validation

PD vs. HC with CNN-DL:

Test accuracy = 80.0%

Test sensitivity = 0.86

Test specificity = 0.70

Test AUC = 0.913

PD vs. APS with CNN-DL:

Test accuracy = 85.7%

Test sensitivity = 1.00

Test specificity = 0.50

Test AUC = 0.911

2019

[21]

Diagnosis,PPMI database

Classification of PD

from HC

RFS-LDA with 10-fold

cross validation

Accuracy = 79.8%

2019

[22]

Diagnosis,PPMI database

Classification of PD

from HC

Naïve Bayes, SVM-RBF

with 10-fold cross

validation

SVM: Accuracy = 87.50%

Sensitivity = 85.00%

Specificity = 90.00%

AUC = 90.00%

2019

[23]

Diagnosis,PPMI database

Classification of PD

and SWEDD from HC

SSAE with 10-fold cross

validation

HC vs. PD:

Accuracy = 85.24%,

88.14%, and 96.19% for

baseline, 12m, and 24m

HC vs. SWEDD:

Accuracy = 89.67%,

95.24%, and 93.10% for

baseline, 12m, and 24m

2019

[24]

Diagnosis,PPMI database

Classification of PD

from HC

CNN (VGG and ResNet)

ResNet50 accuracy = 88.6%

2019

[25]

Conclusion

We presented included studies in a high-level summary, providing a literature review of studies that used machine learning models to diagnose Parkinson\'s disease published in 2019, using the PubMed and IEEE Xplore databases, to provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of Parkinson\'s disease.We evaluated their aims, data sources, data types, machine learning approaches, and associated outcomes in this study.The implementation of machine learning-assisted Parkinson\'s disease diagnosis has a great potential for a more systematic clinical decision-making system, while the adaption of novel biomarkers may lead to simpler access to PD diagnosis at an earlier stage.

References

[2] Vu TC, Nutt JG, Holford NH (2012) Progression of Motor and Non-Motor Features of Parkinson’s Disease and Their Response to Treatment. Br J Clin Pharmacol. [3] B. E. Sakar, M. E. Isenkul, C. O. Sakar, A. Sertbas,F. Gurgen, S. Delil, H. Apaydin, and O.Kursun,“Collection and analysis of a parkinson speech dataset with multiple types of sound recordings,” Journal of IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 4, pp. 828-834, July 2013. [4] Ahmadi, S. A., Vivar, G., Frei, J., Nowoshilow, S., Bardins, S., Brandt, T., et al.(2019). Towards computerized diagnosis of neurological stance disorders: data mining and machine learning of posturography and sway. J. Neurol. 266(Suppl1), 108–117. doi: 10.1007/s00415-019-09458-y [5] Buongiorno, D., Bortone, I., Cascarano, G. D., Trotta, G. F., Brunetti, A., and Bevilacqua, V. (2019). A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s Disease. BMC Med. Inform. Decision Making 19(Suppl 9):243. doi: 10.1186/s12911-019-0987-5 [6] Cavallo, F., Moschetti, A., Esposito, D., Maremmani, C., and Rovini, E. (2019). Upper limb motor pre-clinical assessment in Parkinson’s disease using machine learning. Parkinsonism Relat. Disord. 63, 111–116. doi: 10.1016/j.parkreldis.2019.02.028 [7] Papadopoulos, A., Kyritsis, K., Klingelhoefer, L., Bostanjopoulou, S., Chaudhuri, K.R., and Delopoulos, A. (2019). Detecting Parkinsonian tremor from IMU data collected in-the-wild using deep multiple-instance learning. IEEE J. Biomed. Health Inform. 24, 2559–2569. doi: 10.1109/JBHI.2019.2961748 [8] Reyes, J. F., Montealegre, J. S., Castano, Y. J., Urcuqui, C., and Navarro, A. (2019). “LSTM and convolution networks exploration for Parkinson’s diagnosis,” in 2019 IEEE Colombian Conference on Communications and Computing(COLCOM) (Barranquilla), 1–4. doi: 10.1109/ColComCon.2019.8809160 [9] Ricci, M., Lazzaro, G. D., Pisani, A., Mercuri, N. B., Giannini, F., and Saggio,G. (2020). Assessment of motor impairments in early untreated parkinson’s disease patients: the wearable electronics impact. IEEE J. Biomed. Health Inform. 24, 120–130. doi: 10.1109/JBHI.2019.2903627 [10] Cavallo, F., Moschetti, A., Esposito, D., Maremmani, C., and Rovini, E. (2019). Upper limb motor pre-clinical assessment in Parkinson’s disease using machine learning. Parkinsonism Relat. Disord. 63, 111–116. doi: 10.1016/j.parkreldis.2019.02.028 [11] Alharthi, A. S., and Ozanyan, K. B. (2019). “Deep learning for ground reaction force data analysis: application to wide-area floor sensing,” in 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE) (Vancouver, BC)„ 1401–1406. doi: 10.1109/ISIE.2019.8781511 [12] Andrei, A., T?au?an, A., and Ionescu, B. (2019). “Parkinson’s disease detection from gait patterns,” in 2019 E-Health and Bioengineering Conference (EHB) (Iasi), 1–4. doi: 10.1109/EHB47216.2019.8969942 [13] Félix, J. P., Vieira, F. H. T., Cardoso, Á. A., Ferreira, M. V. G., Franco, R. A. P., Ribeiro, M. A., et al. (2019). “A Parkinson’s disease classification method: an approach using gait dynamics and detrended fluctuation analysis,” in 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) (Edmonton, AB), 1–4. doi: 10.1109/CCECE.2019.8861759 [14] Khoury, N., Attal, F., Amirat, Y., Oukhellou, L., and Mohammed, S. (2019). Datadriven based approach to aid Parkinson’s disease diagnosis. Sensors 19:242. doi: 10.3390/s19020242 [15] Xia, Y., Yao, Z., Ye, Q., and Cheng, N. (2020). A dual-modal attentionenhanced deep learning network for quantification of Parkinson’s disease characteristics. IEEE Trans. Neural Syst. Rehab. Eng. 28, 42–51.doi: 10.1109/TNSRE.2019.2946194 [16] Abos, A., Baggio, H. C., Segura, B., Campabadal, A., Uribe, C., Giraldo, D.M., et al. (2019). Differentiation of multiple system atrophy from Parkinson’s disease by structural connectivity derived from probabilistic tractography. Sci. Rep. 9:16488. doi: 10.1038/s41598-019-52829-8 [17] Abos, A., Baggio, H. C., Segura, B., Campabadal, A., Uribe, C., Giraldo, D.M., et al. (2019). Differentiation of multiple system atrophy from Parkinson’s disease by structural connectivity derived from probabilistic tractography. Sci. Rep. 9:16488. doi: 10.1038/s41598-019-52829-8 [18] Banerjee, M., Chakraborty, R., Archer, D., Vaillancourt, D., and Vemuri, B. C. (2019). “DMR-CNN: a CNN tailored For DMR scans with applications to PD classification,” in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (Venice), 388–391. doi: 10.1109/ISBI.2019.8759558 [19] Kiryu, S., Yasaka, K., Akai, H., Nakata, Y., Sugomori, Y., Hara, S., et al. (2019). Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging: a proof of concept study. Eur. Radiol. 29, 6891–6899. doi: 10.1007/s00330-019-06327-0 [20] Rubbert, C.,Mathys, C., Jockwitz, C., Hartmann, C. J., Eickhoff, S. B., Hoffstaedter, F., et al. (2019). Machine-learning identifies Parkinson’s disease patients based on resting-state between-network functional connectivity. Br. J. Radiol.92:20180886. doi: 10.1259/bjr.20180886 [21] Shinde, S., Prasad, S., Saboo, Y., Kaushick, R., Saini, J., Pal, P. K., et al. (2019). Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI. NeuroImage. Clin. 22:101748.doi: 10.1016/j.nicl.2019.101748 [22] Adeli, E., Thung, K.-H., An, L., Wu, G., Shi, F., Wang, T., et al. (2019). Semi-supervised discriminative classification robust to sample-outliers and feature-noises. IEEE Trans. Pattern Anal. Mach. Intell. 41, 515–522. doi: 10.1109/TPAMI.2018.2794470 [23] Cigdem, O., Demirel, H., and Unay, D. (2019). “The performance of locallearning based clustering feature selection method on the diagnosis of Parkinson’s disease using structural MRI,” in 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (Bari), 1286–1291. doi: 10.1109/SMC.2019.8914611 [24] Haq, A. U., Li, J. P.,Memon,M. H., khan, J., andMalik, A., Ahmad, T., et al. (2019). Feature selection based on L1-Norm support vector machine and effective recognition system for Parkinson’s disease using voice recordings. IEEE Access 7, 37718–37734. doi: 10.1109/ACCESS.2019.2906350 [25] Yagis, E., Herrera, A. G. S. D., and Citi, L. (2019). “Generalization Performance of Deep LearningModels in Neurodegenerative Disease Classification,” in 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (San Diego, CA), 1692–1698. doi: 10.1109/BIBM47256.2019.8983088

Copyright

Copyright © 2022 Md. Toukir Ahmed, Md. Nazrul Islam Mondal, Mohammed Sowket Ali, Md. Moshaheb Hossain, Mahabuba . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

ijraset44340

Download Paper

Authors : Md. Toukir Ahmed

Paper Id : IJRASET44340

Publish Date : 2022-06-15

ISSN : 2321-9653

Publisher Name : IJRASET

DOI Link : Click Here

About Us

International Journal for Research in Applied Science and Engineering Technology (IJRASET) is an international peer reviewed, online journal published for the enhancement of research in various disciplines of Applied Science & Engineering Technologies.

Quick links
  • Privacy Policy
  • Refund & Cancellation Policy
  • Shipping Policy
  • Terms & Conditions
Quick links
  • Home
  • About us
  • Editorial Board
  • Impact Factor
  • Submit Paper
  • Current Issue
  • Special Issue
  • Pay Fee
  • Topics
Journals for publication of research paper | Research paper publishers | Paper publication sites | Best journal to publish research paper | Research paper publication sites | Journals for paper publication | Best international journal for paper publication | Best journals to publish papers in India | Journal paper publishing sites | International journal to publish research paper | Online paper publishing journal

© 2022, International Journal for Research in Applied Science and Engineering Technology All rights reserved. | Designed by EVG Software Solutions