A Review on Parkinson's Disease Detection Methods: Traditional Machine Learning Models vs. Deep Learning Models


  •   Md. Toukir Ahmed

  •   Md. Nazrul Islam Mondal

  •   Debashis Gupta

  •   Mohammed Sowket Ali


Millions of people throughout the world suffer with Parkinson's disease (PD), severely reducing their quality of life. With the symptoms when we detect Parkinson disease automatically, it could provide insights to the disease's early stages of development, enhancing the patients' projected clinical results through correctly focused therapies. This potential has prompted numerous academics to explore ways for measuring and quantifying the existence of PD symptoms using commercially available sensors. In this paper, we offer an overview of some recent scientific articles on several machine learning techniques that assist physiologists in detecting PD early. In addition, a comparative study between traditional machine learning (TML) algorithms and deep learning (DL) architectures based on the scope of their appropriate usage for classifying PD effectively has been discussed. Based on the comparison on detecting the PD from previous works, this paper concludes that deep learning models are more efficacious than traditional machine learning algorithms.

Keywords: Deep learning, diagnosis, machine learning, Parkinson’s disease, traditional machine learning


Wikipedia.org. Parkinson’s disease [Internet]. 2022 [updated 2022 April 28; cited 2022 May 01]. [7 screens]. Available from: https://en.wikipedia.org/wiki/ Parkinson’s disease

Swedish University. Writing literature reviews. [Internet] 2006 [updated 2021 Jan 23; cited 2022 Apr 6]. Available from:https://www.dissertations.se/about/parkinson’s+disease/

University of Oxford. Writing literature reviews. [Internet] 2022 [updated 2022 Feb 22; cited 2022 Apr 6]; Available from: https://www.neuroscience.ox.ac.uk/parkinsons-disease

Wikipedia.org. Signs and symptoms of Parkinson's disease [Internet]. 2022 [updated 2022 April 28; cited 2022 May 01]. [7 screens]. Available from: https://en.wikipedia.org/wiki/ Signs_and_symptoms_of_Parkinson's_disease

Hoehn MM, Yahr MD. Parkinsonism: onset, progression and mortality. Neurology.WNL. 1967; 17(5): 427-442.

Buongiorno D, Bortone I, Cascarano GD, Trotta GF, Brunetti A, Bevilacqua V. 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. 2019; 19(9): 243-244.

Abos A, Baggio HC, Segura B, Campabadal A, Uribe C, Giraldo DM, et al. Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography. Sci. Rep.2019; 9: 16488.

Rovini E, Maremmani C, Moschetti A, Esposito D, Cavallo F. Comparative motor pre-clinical assessment in parkinson's disease using supervised machine learning approaches. Annals Biomed. Eng. 2018; 46: 2057–2068.

Papadopoulos A, Kyritsis K, Klingelhoefer L, Bostanjopoulou S, Chaudhuri KR, Delopoulos A. Detecting Parkinsonian tremor from IMU data collected in-the-wild using deep multiple-instance learning. IEEE J. Biomed. Health Inform. 2019; 24: 2559–2569.

Kiryu S, Yasaka K, Akai H, Nakata Y, Sugomori Y, Hara S, et al. Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging: a proof of concept study. Eur. Radiol. 2019; 29(12): 6891–6899.

Castillo BD, Ramírez J, Segovia F, Martínez-Murcia FJ, Salas GD, Górriz JM. Robust ensemble classification methodology for I123-Ioflupane SPECT images and multiple heterogeneous biomarkers in the diagnosis of Parkinson’s disease. Front. Neuroinf. 2018; 12: 53.

Noble WS. What is a support vector machine?. Nature Biotechnology. 2006; 24(12): 1565–1567.

Ho K. Random decision forests. International Conference on Document Analysis and Recognition. 1995; 1(17): 278–282.

Kalchbrenner N, Grefenstette E, Blunsom P. A convolutional neural network for odelling sentences. arXiv preprint arXiv: 2014: 23(13): 2172-2188.

Finlayson SG, Bowers JD, Ito J,Zittrain JL, Beam AL, Kohane IS. Adversarial attacks on medical machine learning. Science: 2019: 363(6433): 1287–1289.

Pal B, Gupta D, Rashed-Al-Mahfuz M, Alyami SA, and Moni MA. Vulnerability in deep transfer learning models to adversarial fast gradient sign attack for covid-19 prediction from chest radiography images. Applied Sciences. 2021: 11(9): 4233.


How to Cite
Ahmed, M. T., Mondal, M. N., Gupta, D., & Ali, M. (2022). A Review on Parkinson’s Disease Detection Methods: Traditional Machine Learning Models vs. Deep Learning Models. European Journal of Information Technologies and Computer Science, 2(3), 1-6. https://doi.org/10.24018/compute.2022.2.3.67