C.Santhosh Kumar, Department of Computer Science and Engineering, Priyadarshini Engineering College, Tamilnadu, India.
Vishnu Kumar Kaliappan and Rajasekaran Thangaraj, Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India
Pandiyan P, Department of Electrical Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India
Online First : 30 December 2020
Publisher Name : IJAICT India Publications, India.
Print ISBN : 978-81-950008-0-7
Online ISBN : 978-81-950008-1-4
Page :186-189
Abstract
In recent years, there is need for early identification of Parkinson’s disease (PD) based on measuring the features that causes disorders in elderly people. Around 80% of Parkinson’s patients show signs of speech-based disorders in the early stages of the disorder. In this paper, early prediction of Parkinson’s disease based on machine learning is compared between different classification algorithms. The proposed comparative study composed of feature extraction, preprocessing, feature selection and three different classification processes. Baseline features and Iterative Feature selection methods were well thought-out for feature selection process. We compare the performance of classification algorithms used for early prediction of Parkinson’s patients with speech disorders. Naïve Bayes, Multilayer Perceptron, Random Forest and J48 Classification algorithms were used for the categorization of Parkinson's patients in the experimental study. Random Forest and Naïve Bayes classification shows better performance from other two classifiers. 94.1176 % accuracy was obtained from the PD dataset with the smaller number of speech features.
Keywords
Parkinson’s disease, machine learning, Feature selection, Classification.
Cite this article
C.Santhosh Kumar, Vishnu Kumar Kaliappan, Rajasekaran Thangaraj and Pandiyan P, “Comparative Study of Classification Algorithms for Early Identification of Parkinson’s Disease Based on Baseline Speech Features”, Innovations in Information and Communication Technology, pp. 186-189, December 2020.
Copyright
© 2020 C.Santhosh Kumar, Vishnu Kumar Kaliappan, Rajasekaran Thangaraj and Pandiyan P. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
