Enhanced Optimal Feature Selection Techniques for Parkinson’s disease Detection using Machine Learning Algorithms
J. Jayashree1, G. Maheswar Reddy2, M. Sai Pradyumna Reddy3, M. Sai Balaram Reddy4, J. Vijayashree5
1J. Jayashree*, Department of Computer Science and Engineering, VIT, Vellore, (Tamil Nadu), India.
2G. Maheswar Reddy, Department of Computer Science and Engineering, VIT, Vellore, (Tamil Nadu), India.
3M. Sai Pradyumna Reddy, Department of Computer Science and Engineering, VIT, Vellore, (Tamil Nadu), India.
4M. Sai Balaram Reddy, Department of Computer Science and Engineering, VIT, Vellore, (Tamil Nadu), India.
5J. Vijayashree, Department of Computer Science and Engineering, VIT, Vellore, (Tamil Nadu), India.
Manuscript received on January 15, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 4375-4384 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6628029320/2020©BEIESP | DOI: 10.35940/ijeat.C6628.029320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Parkinson disease is a common mass measurement problem in public health. Machine-based learning is used to differentiate between the stable and Parkinson’s disease people. This paper provides a comprehensive review of the Parkinson disease buying estimate using machine-based learning approaches. A brief introduction is given to various methods of artificial intelligence, focused on strategies used to predict Parkinson disease. This paper also offers a study of the results obtained by using MRMR feature selection algorithms with four classifications for Parkinson’s disease detection using python
Keywords: Parkinson Disease(PD), Types of Parkinson’s Disease, Stages, Symptoms, Causes, Risk Factor, Complications, Treatment, Prevention, Statistics, MRMR, python.