Design and Implementation of End to End Application for Parkinson Disease Categorization

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Mohamed Thalhaa.F
Vel Senabathy.S.R


Parkinson’s disease (PD) is one of the major public health disease in the world which is progressively increasing day by day and had its effect on many countries. Thus, it is very important to predict it in early age which has been challenging task among researchers because the symptoms of the disease come into existence in either middle or late middle age. So this Project focuses on the Spiral Test difficulty symptoms of PD affected people and formulates the model using various machine learning techniques such as adaptive boosting,Recurrent Neural Network (RNN), Convolutional Deep Neural Networks, support vector machine, decision tree, Convolutional Neural Networks and linear regression. Performance of these classifiers is evaluated using metric such as. accuracy, Receiver Operating Characteristic curve (ROC), Sensitivity, precision. At last, the Feature selection technique is used to find the most important features among all the features to predict Parkinson’s disease.

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How to Cite
A.Meiappane, Mohamed Thalhaa.F, Mahadevan.R, & Vel Senabathy.S.R. (2023). Design and Implementation of End to End Application for Parkinson Disease Categorization . Journal of Coastal Life Medicine, 11(2), 1556–1563. Retrieved from


Kamal Nayan Reddy, Challa, Venkata Sasank Pagolu and Ganapati Panda, “An Improved Approach for Prediction of Parkinson’s Disease using Machine Learning Techniques”, in Procedings of the International conference on Signal Processing, Communication, Power and Embedded System (SCOPES)-2016, pp. 1446-145, 2016.

Geeta Yadav, Yugal Kumar and G. Sahoo, “Predication of Parkinson’s disease using Data Mining Methods: a comparative analysis of tree, statistical and support vector machine classifiers”, in Procedings of the National Conference on Computing and Communication Systems (NCCCS), pp. 1-4, 2012.

Paolo Bonato, Delsey M. Sherrill, David G. Standaert, Sara S. Salles and Metin Akay, “Data Mining Techniques to Detect Motor Fluctuations in Parkinson's Disease”, in Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4766-4769, 2004.

Sonu S. R., Vivek Prakash and Ravi Ranjan, “Prediction of Parkinson’s Disease using Data Mining”, in Proceedings of the International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 1082-1085, 2017.

Aarushi Agarwal, Spriha Chandrayan and Sitanshu S Sahu, “Prediction of Parkinson’s Disease using Speech Signal with Extreme Learning Machine”, in Proceedings of the International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 1-4, 2016.

Akshaya Dinesh and Jennifer He, “Using Machine Learning to Diagnose Parkinson’s Disease from Drawing Recording”, in Proceedings of the IEEE MIT Undergraduate Research Technology Conference (URTC), pp. 1-4, 2017.

Giulia Fiscon, Emanuel Weitschek, Giovanni Felici and Paola Bertolazzi, “Alzheimer’s disease patients classification through EEG signals processing”, in Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM). pp 1-4, 2014.

Pedro Miguel Rodrigues, Diamantino Freitas and Joao Paulo Teixeirab, “Alzheimer electroencephalogram temporal events detection by K-means”, in Proceedings of the International Conference on Health and Social Care Information Systems and Technologies HCIST. pp. 859 – 864, 2012.

Elva Maria Novoa-del-Toro, Juan Fernandez-Ruiz, Hector Gabriel Acosta-Mesa and Nicandro Cruz-Ramirez, “Applied Macine Learning to Identify Alzheimer's Disease through the Analysis of Magnetic Resonance Imaging”, in Proceedings of the International Conference on Computational Science and Computational Intelligence, pp. 577-582, 2015.

Daniel Johnstone1, Elizabeth A. Milward1, Regina Berretta1 and Pablo Moscato1, “Multivariate Protein Signatures of Pre-Clinical Alzheimer’s Disease in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Plasma Proteome Dataset”, in Proceedings of the Disease Neuroimaging Initiative, vol-7, pp. 1-17, 2017.

Jason Orlosky, Yuta Itoh, Maud Ranchet, Kiyoshi Kiyokawa, John Morgan, and Hannes Devos, “Emulation of Physician Tasks in Eye-tracked Virtual Reality for Remote Diagnosis of Neurodegenerative Disease”, in Proceedings of the IEEE Transactions on Visualization and Computer Graphics, vol. 23, pp. 1302 – 1311, 2017.

Mathew J. Summers, Vienna, Austria, Alessandro E. Vercelli, Georg Aumayr, Doris M. Bleier and Ludovico Ciferri,”Deep Machine Learning Application to the Detection of Preclinical Neurodegenerative Diseases of Aging”, in Proceedings of the Scientific Journal on Digital Cultures, vol. 2, pp. 9-24, 2017.

Bianca Torres, Raquel Luiza Santos, Maria Fernanda Barroso de Sousa, Jose Pedro Simoes Neto, Marcela Moreira Lima Nogueira, Tatiana T. Belfort1, Rachel Dias1, Marcia and Cristina Nascimento Dourado, “Facial expression recognition in Alzheimer’s disease: a longitudinal study”, pp. 383-389, 2014.

Smitha Sunil and Kumaran Nair, “An exploratory study on Big data processing: a case study from a biomedical informatics”, 3rd MEC International Conference on Big Data and Smart City, pp. 1-4, 2016.

C. Kotsavasilogloua, N. Kostikis, D. Hristu-Varsakelis and M. Arnaoutoglouc, “Machine learning-based classification of simple drawing movements in Parkinson’s disease”, in Proceedings of the Biomedical Signal Processing and Control, pp. 174–180, 2017.

Santosh S. Rathore and Sandeep Kumar, “An empirical study of some software fault prediction techniques for the number of faults prediction”, in Proceedings of the Soft Computing, vol. 21, pp 7417–7434, 2017.

Arvind Kumar Tiwari, “Machine Learning Based Approaches for Prediction of Parkinson’s Disease”, in Proceedings of the Machine Learning and Applications: An International Journal (MLAIJ), vol.3, pp. 33-39, 2016.

Polina Mamoshina, Armando Vieira, Evgeny Putin and Alex Zhavoronkov, “Applications of Deep Learning in Biomedicine”, in Proceedings of the American Chemical Sociecty Mol. Pharmaceutics, pp. 1445−1454, 2016.

Alexis Elbaz,James H. Bower, Brett J. Peterson, Demetrius M. Maraganore, Shannon K. McDonnell, J. Eric Ahlskog, Daniel J. Schaid, Walter A. Rocca, “Survival Study of Parkinson Disease in Olmsted County, Minnesota “, Arch Neurol. Vol. 60 pp. 91-96, 2003.

Tanner CM, Ross GW, Jewell SA ,” Occupation and risk of Parkinsonism: a multicenter

V. A. Sukhanov, I. D. Ionov, and L. A. Piruzyan, “Neurodegenerative Disorders: The Role of Genetic Factors in Their Origin and the Efficiency of Treatment” in Proceedings of the Human Physiology US National Library of Medicine National Institutes of Health, vol. 31, pp. 472–482, 2005. about-Parkinsonsdisease

Marras C, Tanner C.”Epidemiology of Parkinson's Disease”,Movement Disorders: Neurologic Principles and Practice, 2nd ed.2004, Watts, RL, Koller, WC (Eds). The McGraw- Hill Companies:New York, pp. 177.

Cnockaert, L., Schoentgen, J., Auzou, P., Ozsancak, C.,Defebvre, L., & Grenez, F., “Low frequency vocal modulations in vowels produced by Parkinsonian subjects”, Speech Communications, vol 50, pp. 288-300, 2008.

Kenneth Revett, Florin Gorunescu and Abdel-Badeeh Mohamed Salem, “Feature Selection in Parkinson’s disease: A Rough Sets Approach”, Proceedings of the International Multi onference on Computer Science and Information Technology, pp. 425 – 428,2004, ISBN 978-83- 60810- 22-4.