Application of Data mining in Analysis and detection of Parkinson’s Disease
DOI:
https://doi.org/10.61841/nmpb6z56Keywords:
SVM, keystroke, Logistic regression, data mining, Parkinson’s diseaseAbstract
Parkinson's disease (PD) is a neurodegenerative disorder which often affects patients' movements. Some of the most common symptoms of Parkinson’s disease are tremors, rigidity, akinesia, walking disability, and postural instability. The primary motor symptoms are collectively called “parkinsonism”. This paper provides a brief description of the existing techniques used in detecting Parkinson’s Disease with the help of various data mining algorithms such as Multiple Instance Learning (MIL), K-means clustering, Decision Tree Classification, Moving Average Algorithm etc., their accuracies and drawbacks and also gives an outline of the proposed system. Since all of the existing models consider a single symptom for detecting Parkinson’s, the proposed approach aims at building an analytical model with two different symptoms i.e. speech and finger tapping keystroke, so as to increase the accuracy and find the co-relation between these symptoms.
Downloads
References
1. https://pn.bmj.com/content/15/1/14
2. “Data mining techniques to detect motor fluctuations in Parkinson's disease”;2005; P. Bonato;
D.M. Sherrill; D.G. Standaert; S.S. Salles; M. Akay;IEEE.
3. “Towards a sensing system for quantification of pathological tremor", F. Widjaja; C. Y. Shee;
W. L. Au; P. Poignet; W. T. Ang; 2007 ;IEEE.
4. “Detecting Parkinsons' symptoms in uncontrolled home environments, A multipleinstance learning approach”; Samarjit Das; Breogan Amoedo; Fernando De laTorre; Jessica Hodgins; 2012; IEEE.
5. “Analysis of visually guided tracking performance in Parkinson's disease”; Yi Liu; Chonho Lee; Bu-Sung Lee; James K.R. Stevenson; Martin J. McKeown; 2014; IEEE. [6] “Classification and visualization tool for gait analysis of Parkinson's disease”; U Kit Pun; Huanying Gu; Ziqian Dong; N. Sertac Artan; IEEE.
6. “Decision Support Framework for Parkinson’s Disease Based on Novel Handwriting Markers”; Peter Drotár; Jiří Mekyska; Irena Rektorová; Lucia Masarová; Zdeněk Smékal; Marcos Faundez-Za; 2015
7. “Using wearable sensors to predict the severity of symptoms and motor complications in late stage Parkinson's Disease” Shyamal Patel; Richard Hughes; Nancy Huggins; David Standaert; John Growdon; Jennifer Dy; 2008; IEEE.
8. “Assessment and visualization of Parkinson's disease tremor”; J. Synnott; L.Chen; C.D. Nugent; G. Moore; 2011;IEEE.
9. “An Emerging Era in the Management of Parkinson's Disease: Wearable Technologies and the Internet of Things”; Cristian F. Pasluosta; Heiko Gassner; Juergen Winkler; Jochen Klucken; Bjoern M. Eskofier;2015; IEEE.
10. 'Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007); BMC geriatrics.
11. http://archive.ics.uci.edu/ml/datasets/Parkinsons
12. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation101(23):e215-e220 [Circulation Electronic Pages].
13. http://circ.ahajournals.org/content/101/23/e215
14. Baken RJ, Orlikoff RF: “Clinical Measurement of Speech and Voice. 2nd edition. San Diego: Singular Thomson Learning”; 2000.
15. “Correlation between motor systems across different Motor tasks, quantified via Random Forest Feature classification in Parkinson’s Disease”; Andreas Kuhner, Tobias Schubert, Massimo Cenciarini, Isabella Katharina Wiesmeier, Volker Arnd Coenen, Wolfram Burgard, Cornelius Weiller, Christoph Maurer.;2007; Front.
16. “Assessment of fall-related self-efficacy and activity avoidance in people with Parkinson's disease”; Maria H Nilsson, Anna-Maria Drake, and Peter Hagel; 2010 in BMC Geriatrics.
17. “Factors associated with freezing of gait in patients with Parkinson’s disease”; SunEul Choi, Hyunjean Jung, Byeong C. Kim, Geum-Jin Yoon; Neurological Sciences in2018 [19]https://docs.opencv.org/2.4/doc/tutorials/ml/introduction_to_svm/introduction_to_svmhtml
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
