Author(s): Basil K Varghese, Geraldine Bessie Amali D, Uma Devi K S

Email(s): geraldine.amali@vit.ac.in

DOI: 10.5958/0974-360X.2019.00114.8   

Address: Basil K Varghese, Geraldine Bessie Amali D*, Uma Devi K S
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
*Corresponding Author

Published In:   Volume - 12,      Issue - 2,     Year - 2019


ABSTRACT:
In the present decade of accelerated advances in Medical Sciences, most studies fail to lay focus on ageing diseases. These are diseases that display their symptoms at a much advanced stage and makes a complete recovery almost improbable. Parkinson’s disease (PD) is the second most commonly diagnosed neurodegenerative disorder of the brain. One could argue, that it is almost incurable and inflicts a lot of pain on the patients. All these make it quite clear that there is an oncoming need for efficient, dependable and expandable diagnosis of Parkinson’s disease. A dilemma of this intensity requires the automating of the diagnosis to lead accurate and reliable results. It has been observed that most PD Patients demonstrate some sort of impairment in speech or speech dysphonia, which makes speech measurements and indicators one of the most important aspects in prediction of PD. The aim of this work is to compare various machine learning models in the successful prediction of the severity of Parkinson’s disease and develop an effective and accurate model in order to help diagnose the disease accurately at an earlier stage which could in turn help the doctors to assist in the cure and recovery of PD Patients. For the aforementioned purpose we plan on using the Parkinson’s Tele monitoring dataset which was acquired from the UCIML repository.


Cite this article:
Basil K Varghese, Geraldine Bessie Amali D, Uma Devi K S. Prediction of Parkinson’s Disease using Machine Learning Techniques on Speech dataset. Research J. Pharm. and Tech 2019; 12(2):644-648. doi: 10.5958/0974-360X.2019.00114.8

Cite(Electronic):
Basil K Varghese, Geraldine Bessie Amali D, Uma Devi K S. Prediction of Parkinson’s Disease using Machine Learning Techniques on Speech dataset. Research J. Pharm. and Tech 2019; 12(2):644-648. doi: 10.5958/0974-360X.2019.00114.8   Available on: https://rjptonline.org/AbstractView.aspx?PID=2019-12-2-33


Recomonded Articles:

Author(s): Sunanda Rao, Lakshmi T.

DOI:         Access: Open Access Read More

Author(s): Peethala Prathyusha, Raja Sundararajan, Palyam Bhanu, Mathrusri Annapurna Mukthinuthalapati

DOI: 10.5958/0974-360X.2020.00507.7         Access: Open Access Read More

Author(s): V. Shirisha, Somsubhra Ghosh, B. Rajni, David Banji

DOI: Not Available         Access: Open Access Read More

Author(s): R Deveswaran, S Bharath, BV Basavaraj, Sindhu Abraham, Sharon Furtado, V Madhavan

DOI:         Access: Open Access Read More

Author(s): Mythili. L, GNK. Ganesh, C. Monisha, Kayalvizhi. R

DOI: 10.5958/0974-360X.2019.00426.8         Access: Open Access Read More

Author(s): Mistry Khushboo, Kavya Naik, Vasanthi, Alicia Menezes, Anup Naha, K.B. Koteshwara, K. Girish Pai

DOI: 10.5958/0974-360X.2017.00540.6         Access: Open Access Read More

Author(s): Sandesh More, Javed Mirza, Nanasaheb Kale, Mayur Gandhi, Rakesh Chaudhari

DOI: Not Available         Access: Open Access Read More

Author(s): Basil K Varghese, Geraldine Bessie Amali D, Uma Devi K S

DOI: 10.5958/0974-360X.2019.00114.8         Access: Open Access Read More

Author(s): Poonam Karekar, Nitin Salunkhe, Adhikrao Yadav, Dnyaneshwar Bangar, Dhanashri Yadav

DOI: Not Available         Access: Open Access Read More

Author(s): T. Arunkumar, Ann Feba Ebby, G. Narendrakumar

DOI: 10.5958/0974-360X.2017.00441.3         Access: Open Access Read More

Author(s): Mahadeva Rao U.S., Suganya M. Utharkar, C. Shanmuga Sundaram

DOI: 10.5958/0974-360X.2015.00307.8         Access: Open Access Read More

Research Journal of Pharmacy and Technology (RJPT) is an international, peer-reviewed, multidisciplinary journal.... Read more >>>

RNI: CHHENG00387/33/1/2008-TC                     
DOI: 10.5958/0974-360X 

0.38
2018CiteScore
 
56th percentile
Powered by  Scopus


SCImago Journal & Country Rank


Recent Articles




Tags