Please use this identifier to cite or link to this item:
Title: Comparative Survey of Machine Learning Techniques for Prediction of Parkinson's Disease
Authors: Saxena M
Ahuja S.
Keywords: classifiers
clustering algorithms
data analysis
learning algorithms
machine learning
Parkinson's disease
supervised machine learning.
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: Prognosis and progression of Parkinson's disease is a critical question among the clinicians since there is a disparity of parameters taken into the diagnostic consideration thereby making the decision process difficult. Different datasets have been independently explored and applied through machine learning to analyze the incidence of occurrence and progression of the disease. The present paper is an updated report of the types of Supervised Machine Learning algorithms which have gained prominence within a span of last 5 years (2015- 2019). Further it highlights the use of hybrid intelligence models to improve the prediction accuracy and sensitivity over standalone methods. Conclusively the paper also emphasis on the need of development of multiparametric, big data based holistic predictive system.
URI: 10.1109/Indo-TaiwanICAN48429.2020.9181368
Appears in Collections:Conferences

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.