Skip to main content
Springer Nature Link
Log in

Machine learning and deep learning approach to Parkinson’s disease detection: present state-of-the-art and a bibliometric review

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Parkinson’s disease (PD) is fatal, severe and irreversible neurological disorder. With the aging of the world population, the prevalence of PD is on the rise, making it the second most common neurodegenerative condition after Alzheimer's. Early recognition can be tricky because the disease's phenotype is essential for anticipating and halting its course. If PD is detected early and accurately, medical therapies may be initiated that may enhance the patient's quality of life and minimize its course. Medical image analysis, diagnostics, and medical management has been greatly improved with Machine Learning (ML) and Deep Learning (DL) based models. Our research analyzed present state-of-art and also the bibliometric review regarding ML and DL techniques used for Parkinson’s Disease Detection (PDD) in order to measure the effectiveness of these technologies. This study analyses the various algorithms, modalities used, their limitations and possible future direction of ML/DL in PDD and at the same time, presents the quantitative analysis of publications in the mentioned research area. We have searched the Scopus database using major and auxiliary keywords pertaining to PD detection using ML and DL research articles published between 2013 and 2023. Based on the keyword input, 1674 articles were returned for bibliometric analysis. In order to visualize and analyze bibliometric networks, software tools such as VOSviewer and Biblioshiny are being used. Additionally, the article offers perceptions on networks of collaboration and bibliographic coupling. Our study will help researchers to easily identify techniques being used, relationships between publications and authors, uncovering new research opportunities and knowledge in ML/DL based PDD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

(Source: Scopus)

Fig. 4

(Source: Scopus, Software: Biblioshiny)

Fig. 5

(Source: Scopus, Software: Biblioshiny)

Fig. 6

(Source: Scopus, Software: Biblioshiny)

Fig. 7

(Source: Scopus, Software: Biblioshiny)

Fig. 8

(Source: Scopus, Software: Biblioshiny)

Fig. 9

(Source: Scopus, Software: VOSviewer)

Fig. 10

(Source: Scopus, Software: Biblioshiny)

Fig. 11

(Source: Scopus, Software: VOSviewer)

Fig. 12

(Source: Scopus, Software: VOSviewer)

Fig. 13

(Source: Scopus, Software: VOSviewer)

Fig. 14

(Source: Scopus, Software: VOSviewer)

Fig. 15

(Source: Scopus, Software: VOSviewer)

Fig. 16

(Source: Scopus, Software: VOSviewer)

Fig. 17

(Source: Scopus, Software: Biblioshiny)

Similar content being viewed by others

Data availability

Data used for bibliometric analysis is available on request from the authors.

References

  1. Parkinson J (2002) An essay on the shaking palsy. J Neuropsychiatry Clin Neurosci 14(2):223–236

    Article  Google Scholar 

  2. Sacristán HE, Serra Fulles JA (2023) The Essentials in Parkinson’s Disease. EC Neurology 15:36–67

    Google Scholar 

  3. Dorsey, E. A., Constantinescu, R., Thompson, J. P., Biglan, K. M., Holloway, R. G., Kieburtz, K., ... & Tanner, C. M. (2007). Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology 68(5), 384–386

  4. Elbaz, A., Bower, J. H., Maraganore, D. M., McDonnell, S. K., Peterson, B. J., Ahlskog, J. E., ... & Rocca, W. A. (2002). Risk tables for parkinsonism and Parkinson's disease. J Clin Epidemiol 55(1), 25–31. https://doi.org/10.1016/S0895-4356(01)00425-5

  5. Bloem BR, Okun MS, Klein C (2021) Parkinson’s disease. Lancet 397(10291):2284–2303. https://doi.org/10.1016/S0140-6736(21)00218-X

    Article  Google Scholar 

  6. Samii A, Nutt JG, Ransom BR (2004) Parkinson’s disease. Lancet 363(9423):1783–1793. https://doi.org/10.1016/S0140-6736(04)16305-8

    Article  Google Scholar 

  7. Pahuja G, Nagabhushan TN (2021) A comparative study of existing machine learning approaches for Parkinson’s disease detection. IETE J Res 67(1):4–14. https://doi.org/10.1080/03772063.2018.1531730

    Article  Google Scholar 

  8. Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, LaPelle N (2008) Movement disorder society-sponsored revision of the unified parkinson’s disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord: official journal of the Movement Disorder Society 23(15):2129–2170. https://doi.org/10.1002/mds.22340

    Article  Google Scholar 

  9. Santos MK, Ferreira Júnior JR, Wada DT, Tenório APM, Nogueira-Barbosa MH, Marques PMDA (2019) Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiol Bras 52:387–396. https://doi.org/10.1590/0100-3984.2019.0049

    Article  Google Scholar 

  10. Lilhore UK, Simaiya S, Kaur A, Prasad D, Khurana M, Verma DK, Hassan A (2021) Impact of deep learning and machine learning in industry 4.0: Impact of deep learning. Cyber-Physical IoT Auton Syst Ind 4.0:179–197 CRC Press

    Google Scholar 

  11. Dhiman P, Kukreja V, Manoharan P, Kaur A, Kamruzzaman MM, Dhaou IB, Iwendi C (2022) A novel deep learning model for detection of severity level of the disease in citrus fruits. Electronics 11(3):495. https://doi.org/10.3390/electronics11030495

    Article  Google Scholar 

  12. Trivedi, N. K., Gautam, V., Anand, A., Aljahdali, H. M., Villar, S. G., Anand, D., ... & Kadry, S. (2021). Early detection and classification of tomato leaf disease using high-performance deep neural network. Sensors 21(23): 7987. https://doi.org/10.3390/s21237987

  13. Trivedi NK, Tiwari RG, Anand A, Gautam V, Witarsyah D, Misra A (2022, November) Application of machine learning for diagnosis of liver cancer. In 2022 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS) (pp. 1–5). IEEE. https://doi.org/10.1109/ICADEIS56544.2022.10037379

  14. Pereira CR, Pereira DR, Rosa GH, Albuquerque VH, Weber SA, Hook C, Papa JP (2018) Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson’s disease identification. Artif Intell Med 87:67–77. https://doi.org/10.1016/j.artmed.2018.04.001

    Article  Google Scholar 

  15. Senturk ZK (2020) Early diagnosis of Parkinson’s disease using machine learning algorithms. Med Hypotheses 138:109603. https://doi.org/10.1016/j.mehy.2020.109603

    Article  Google Scholar 

  16. Ricci M, Di Lazzaro G, Pisani A, Mercuri NB, Giannini F, Saggio G (2019) Assessment of motor impairments in early untreated Parkinson’s disease patients: the wearable electronics impact. IEEE J Biomed Health Inform 24(1):120–130. https://doi.org/10.1109/JBHI.2019.2903627

    Article  Google Scholar 

  17. Chakraborty S, Aich S, Kim HC (2020) 3D textural, morphological and statistical analysis of voxel of interests in 3T MRI scans for the detection of Parkinson’s disease using artificial neural networks. In Healthcare 8(1):34. https://doi.org/10.3390/healthcare8010034. MDPI

    Article  Google Scholar 

  18. Maass, F., Michalke, B., Willkommen, D., Leha, A., Schulte, C., Tönges, L., ... & Lingor, P. (2020). Elemental fingerprint: Reassessment of a cerebrospinal fluid biomarker for Parkinson's disease. Neurobiol Dis 134: 104677. https://doi.org/10.1016/j.nbd.2019.104677

  19. Nuvoli S, Spanu A, Fravolini ML, Bianconi F, Cascianelli S, Madeddu G, Palumbo B (2020) [123 I] Metaiodobenzylguanidine (MIBG) Cardiac Scintigraphy and Automated Classification Techniques in Parkinsonian Disorders. Mol Imag Biol 22:703–710. https://doi.org/10.1007/s11307-019-01406-6

    Article  Google Scholar 

  20. Váradi C, Nehéz K, Hornyák O, Viskolcz B, Bones J (2019) Serum N-glycosylation in Parkinson’s disease: a novel approach for potential alterations. Molecules 24(12):2220. https://doi.org/10.3390/molecules24122220

    Article  Google Scholar 

  21. Mei J, Desrosiers C, Frasnelli J (2021) Machine learning for the diagnosis of Parkinson’s disease: a review of literature. Front Aging Neurosci 13:633752. https://doi.org/10.3389/fnagi.2021.633752

    Article  Google Scholar 

  22. Gerraty RT, Provost A, Li L, Wagner E, Haas M, Lancashire L (2023) Machine learning within the Parkinson’s progression markers initiative: review of the current state of affairs. Front Aging Neurosci 15:1076657. https://doi.org/10.3389/fnagi.2023.1076657

    Article  Google Scholar 

  23. Shaban M (2023) Deep learning for Parkinson’s disease diagnosis: a short survey. Computers 12(3):58. https://doi.org/10.3390/computers12030058

    Article  MathSciNet  Google Scholar 

  24. Suganya A, Aarthy SL (2023) Application of deep learning in the diagnosis of Alzheimer’s and Parkinson’s disease-a review. Curr Med Imaging. https://doi.org/10.2174/1573405620666230328113721

    Article  Google Scholar 

  25. Shetty S, Rao YS (2016) SVM based machine learning approach to identify Parkinson's disease using gait analysis. In 2016 International conference on inventive computation technologies (ICICT) 2: 1–5 IEEE

  26. Taleb C, Khachab M, Mokbel C, Likforman-Sulem L (2017) Feature selection for an improved Parkinson's disease identification based on handwriting. In 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR). 52–56. IEEE.

  27. Vanegas MI, Ghilardi MF, Kelly SP, Blangero A (2018) Machine learning for EEG-based biomarkers in Parkinson’s disease. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2661–2665. IEEE. https://doi.org/10.1109/BIBM.2018.8621498

  28. Wroge TJ, Özkanca Y, Demiroglu C, Si D, Atkins DC, Ghomi RH (2018) Parkinson’s disease diagnosis using machine learning and voice. In 2018 IEEE signal processing in medicine and biology symposium (SPMB) 1–7. IEEE. https://doi.org/10.1109/SPMB.2018.8615607

  29. Almeida JS, Rebouças Filho PP, Carneiro T, Wei W, Damaševičius R, Maskeliūnas R, de Albuquerque VHC (2019) Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques. Pattern Recogn Lett 125:55–62. https://doi.org/10.1016/j.patrec.2019.04.005

    Article  Google Scholar 

  30. Karabayir I, Goldman SM, Pappu S, Akbilgic O (2020) Gradient boosting for Parkinson’s disease diagnosis from voice recordings. BMC Med Inform Decis Mak 20(1):1–7

    Article  Google Scholar 

  31. Talitckii, A., Kovalenko, E., Anikina, A., Zimniakova, O., Semenov, M., Bril, E., ... & Somov, A. (2020). Avoiding misdiagnosis of Parkinson’s disease with the use of wearable sensors and artificial intelligence. IEEE Sens J 21(3): 3738–3747. https://doi.org/10.1109/JSEN.2020.3027564

  32. Markello RD, Shafiei G, Tremblay C, Postuma RB, Dagher A, Misic B (2021) Multimodal phenotypic axes of Parkinson’s disease. npj Parkinson's Disease, 7(1):6. https://doi.org/10.1038/s41531-020-00144-9

  33. Ahmed I, Aljahdali S, Khan MS, Kaddoura S (2022) Classification of Parkinson disease based on patient’s voice signal using machine learning. Intell Autom Soft Comput 32(2):705. https://doi.org/10.32604/iasc.2022.022037

    Article  Google Scholar 

  34. Li A, Li C (2022) Detecting parkinson’s disease through gait measures using machine learning. Diagnostics 12(10):2404

    Article  Google Scholar 

  35. Weintraub, D., Posavi, M., Fontanillas, P., Tropea, T. F., Mamikonyan, E., Suh, E., ... & Chen‐Plotkin, A. S. (2022). Genetic prediction of impulse control disorders in Parkinson's disease. Annal Clin Transl Neurol 9(7): 936–949. https://doi.org/10.1002/acn3.51569

  36. Pantaleo, E., Monaco, A., Amoroso, N., Lombardi, A., Bellantuono, L., Urso, D., ... & Bellotti, R. (2022). A machine learning approach to Parkinson’s disease blood transcriptomics. Genes 13(5): 727. https://doi.org/10.3390/genes13050727

  37. Alshammri R, Alharbi G, Alharbi E, Almubark I (2023) Machine learning approaches to identify Parkinson’s disease using voice signal features. Front Artif Intell 6:1084001. https://doi.org/10.3389/frai.2023.1084001

    Article  Google Scholar 

  38. Chaurasia V, Chaurasia A (2023) Detection of Parkinson's disease by using machine learning stacking and ensemble method. Biomed Mater Devices 1–13. https://doi.org/10.1007/s44174-023-00079-8

  39. Elshewey AM, Shams MY, El-Rashidy N, Elhady AM, Shohieb SM, Tarek Z (2023) Bayesian optimization with support vector machine model for parkinson disease classification. Sensors 23(4):2085. https://doi.org/10.3390/s23042085

    Article  Google Scholar 

  40. Cesarelli, G., Donisi, L., Amato, F., Romano, M., Cesarelli, M., D’Addio, G., ... & Ricciardi, C. (2023). Using features extracted from upper limb reaching tasks to detect Parkinson’s disease by means of machine learning models. IEEE Trans Neural Syst Rehabil Eng 31: 1056–1063. https://doi.org/10.1109/TNSRE.2023.3236834

  41. Martinez-Eguiluz, M., Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Perona, I., Murueta-Goyena, A., ... & Gabilondo, I. (2023). Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies. Neural Comput Appl 35(8): 5603–5617

  42. Bao Y, Wang L, Yu F, Yang J, Huang D (2023) Parkinson’s disease gene biomarkers screened by the LASSO and SVM algorithms. Brain Sci 13(2):175. https://doi.org/10.3390/brainsci13020175

    Article  Google Scholar 

  43. Govindu A, Palwe S (2023) Early detection of Parkinson’s disease using machine learning. Procedia Comput Sci 218:249–261. https://doi.org/10.1016/j.procs.2023.01.007

    Article  Google Scholar 

  44. Oh SL, Hagiwara Y, Raghavendra U, Yuvaraj R, Arunkumar N, Murugappan M, Acharya UR (2020) A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput Appl 32:10927–10933

    Article  Google Scholar 

  45. Shi X, Wang T, Wang L, Liu H, Yan N (2019) Hybrid Convolutional Recurrent Neural Networks Outperform CNN and RNN in Task-state EEG Detection for Parkinson's Disease. In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) 939–944. IEEE. https://doi.org/10.1109/APSIPAASC47483.2019.9023190

  46. Zhang X, Yang Y, Wang H, Ning S, Wang H (2019) Deep neural networks with broad views for Parkinson's disease screening. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 1018–1022. IEEE. https://doi.org/10.1109/BIBM47256.2019.8983000

  47. Gunduz H (2019) Deep learning-based Parkinson’s disease classification using vocal feature sets. IEEE Access 7:115540–115551. https://doi.org/10.1109/ACCESS.2019.2936564

    Article  Google Scholar 

  48. Rasheed J, Hameed AA, Ajlouni N, Jamil A, Özyavaş A, Orman Z (2020) Application of adaptive back-propagation neural networks for Parkinson’s disease prediction. In 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI) 1–5. IEEE. https://doi.org/10.1109/ICDABI51230.2020.9325709

  49. Ramírez VM, Kmetzsch V, Forbes F, Dojat M (2020) Deep learning models to study the early stages of Parkinson's disease. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) 1534–1537. IEEE. https://doi.org/10.1109/ISBI45749.2020.9098529

  50. Pfister, F. M., Um, T. T., Pichler, D. C., Goschenhofer, J., Abedinpour, K., Lang, M., ... & Fietzek, U. M. (2020). High-resolution motor state detection in Parkinson’s disease using convolutional neural networks. Sci Rep 10(1): 5860

  51. Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G (2020) Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput Appl 32:839–854

    Article  Google Scholar 

  52. Khare SK, Bajaj V, Acharya UR (2021) PDCNNet: An automatic framework for the detection of Parkinson’s disease using EEG signals. IEEE Sens J 21(15):17017–17024. https://doi.org/10.1109/JSEN.2021.3080135

    Article  Google Scholar 

  53. Shaban M (2021) Automated screening of Parkinson's disease using deep learning based electroencephalography. In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) 158–161. IEEE. https://doi.org/10.1109/NER49283.2021.9441065

  54. Kamran I, Naz S, Razzak I, Imran M (2021) Handwriting dynamics assessment using deep neural network for early identification of Parkinson’s disease. Futur Gener Comput Syst 117:234–244. https://doi.org/10.1016/j.future.2020.11.020

    Article  Google Scholar 

  55. Oğul BB, Özdemir S (2021) A pairwise deep ranking model for relative assessment of Parkinson’s disease patients from gait signals. IEEE Access 10:6676–6683. https://doi.org/10.1109/ACCESS.2021.3136724

    Article  Google Scholar 

  56. Yang X, Ye Q, Cai G, Wang Y, Cai G (2022) PD-ResNet for classification of Parkinson’s disease from gait. IEEE J Transl Eng Health Med 10:1–11. https://doi.org/10.1109/JTEHM.2022.3180933

    Article  Google Scholar 

  57. Shaban M, Amara AW (2022) Resting-state electroencephalography based deep-learning for the detection of Parkinson’s disease. PLoS One 17(2):e0263159. https://doi.org/10.1371/journal.pone.0263159

    Article  Google Scholar 

  58. Mahmood A, Mehroz Khan M, Imran M, Alhajlah O, Dhahri H, Karamat T (2023) End-to-end deep learning method for detection of invasive Parkinson’s disease. Diagnostics 13(6):1088

    Article  Google Scholar 

  59. Abdullah SM, Abbas T, Bashir MH, Khaja IA, Ahmad M, Soliman NF, El-Shafai W (2023) Deep transfer learning based parkinson’s disease detection using optimized feature selection. IEEE Access 11:3511–3524. https://doi.org/10.1109/ACCESS.2023.3233969

    Article  Google Scholar 

  60. Li H, He Q, Wu L (2023) Detection of brain abnormalities in Parkinson’s rats by combining deep learning and motion tracking. IEEE Trans Neural Syst Rehabil Eng 31:1001–1007. https://doi.org/10.1109/TNSRE.2023.3237916

    Article  Google Scholar 

  61. Zhang YN (2017) Can a smartphone diagnose Parkinson disease? a deep neural network method and telediagnosis system implementation. Parkinson’s disease. https://doi.org/10.1155/2017/6209703

  62. Kaiser, S., Zhang, L., Mollenhauer, B., Jacob, J., Longerich, S., Del-Aguila, J., ... & Serrano-Fernandez, P. (2023). A proteogenomic view of Parkinson’s disease causality and heterogeneity. npj Parkinson's Dis 9(1): 24

  63. Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2921–2929).

  64. Sundararajan M, Taly A Yan Q (2017) Axiomatic attribution for deep networks. Proceedings of the 34th International Conference on Machine Learning, in Proceedings of Machine Learning Research 70:3319–3328 Available from https://proceedings.mlr.press/v70/sundararajan17a.html

  65. Chahine LM, Amara AW, Videnovic A (2017) A systematic review of the literature on disorders of sleep and wakefulness in Parkinson’s disease from 2005 to 2015. Sleep Med Rev 35:33–50. https://doi.org/10.1016/j.smrv.2016.08.001

    Article  Google Scholar 

  66. Aygun D (2018) Sleep disorders in Parkinson’s disease in Parkinson’s Disease—Understanding Pathophysiology and Developing Therapeutic Strategies. Intechopen: London, UK

  67. Sara SJ (2017) Sleep to remember. J Neurosci 37(3):457–463. https://doi.org/10.1523/JNEUROSCI.0297-16.2017

    Article  Google Scholar 

  68. Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., ... & Band, S. S. (2023). Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues. Cluster Comput 26(4): 2271–2311

  69. Gibson E, Daim T, Garces E, Dabic M (2018) Technology foresight: A bibliometric analysis to identify leading and emerging methods. Фopcaйт 12(1 (eng)): 6–24

  70. Cai Y, Lu W, Wang L, Xing W (2015) Cloud computing research analysis using bibliometric method. Int J Software Eng Knowl Eng 25(03):551–571. https://doi.org/10.1142/S0218194015400203

    Article  Google Scholar 

  71. Wang C, Lim MK, Lyons A (2019) Twenty years of the international journal of logistics research and applications: a bibliometric overview. Int J Log Res Appl 22(3):304–323. https://doi.org/10.1080/13675567.2018.1526262

    Article  Google Scholar 

  72. Forliano C, De Bernardi P, Yahiaoui D (2021) Entrepreneurial universities: A bibliometric analysis within the business and management domains. Technol Forecast Soc Chang 165:120522. https://doi.org/10.1016/j.techfore.2020.120522

    Article  Google Scholar 

  73. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res 133:285–296. https://doi.org/10.1016/j.jbusres.2021.04.070

    Article  Google Scholar 

  74. Karger E, Kureljusic M (2023) Artificial intelligence for cancer detection—a bibliometric analysis and avenues for future research. Curr Oncol 30(2):1626–1647. https://doi.org/10.3390/curroncol30020125

    Article  Google Scholar 

  75. Pei X, Zuo K, Li Y, Pang Z (2023) A review of the application of multi-modal deep learning in medicine: bibliometrics and future directions. Int J Comput Intell Syst 16(1):44. https://doi.org/10.1007/s44196-023-00225-6

    Article  Google Scholar 

  76. Ahsan MM, Siddique Z (2022) Machine Learning-Based Disease Diagnosis: A Bibliometric Analysis. arXiv preprintarXiv:2201.02755. https://doi.org/10.48550/arXiv.2201.02755

  77. Paul J, Criado AR (2020) The art of writing literature review: What do we know and what do we need to know? Int Bus Rev 29(4):101717. https://doi.org/10.1016/j.ibusrev.2020.101717

    Article  Google Scholar 

  78. Aria M, Cuccurullo C (2020) Bilionshiny biliomertrix

  79. Van Eck NJ, Waltman L (2020) VOSviewer Manual.[Online] Available at: https://www.vosviewer.com/documentation

  80. Elango B, Rajendran P (2012) Authorship trends and collaboration pattern in the marine sciences literature: a scientometric study. Int J Inf Dissem Technol 2(3):166–169

    Google Scholar 

  81. Small H (1973) Co-citation in the scientific literature: A new measure of the relationship between two documents. J Am Soc Inform Sci 24(4):265–269

    Article  Google Scholar 

  82. Ferreira MP, Reis NR, Miranda R (2015) Thirty years of entrepreneurship research published in top journals: analysis of citations, co-citations and themes. J Glob Entrep Res 5:1–22

    Article  Google Scholar 

Download references

Acknowledgements

We are thankful to the referee for their guidance and support.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Gauri Sabherwal & Amandeep Kaur

Authors
  1. Gauri Sabherwal
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Amandeep Kaur
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.

Corresponding author

Correspondence to Gauri Sabherwal.

Ethics declarations

Conflict of interest

The authors have no funding or conflict of interest to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sabherwal, G., Kaur, A. Machine learning and deep learning approach to Parkinson’s disease detection: present state-of-the-art and a bibliometric review. Multimed Tools Appl 83, 72997–73030 (2024). https://doi.org/10.1007/s11042-024-18398-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-024-18398-3

Keywords