Hybrid optimization enabled deep learning model for Parkinson's disease classification

ABSTRACT

The analysis of Parkinson's disease (PD) is an inspiring task that necessitates the analysis of numerous motor and non-motor indications. During analysis, some abnormalities are considered as important symptoms to analyze the disease. Hence, this research introduced the proposed chronological smart sunflower optimization Algorithm (CSSFOA) for classifying the PD from voice data and voice signal samples. For voice signal, the input signals are pre-processed by the Gaussian filter, and then the significant features are extracted from it. The selection of optimal features is done by the chronological smart flower optimization Algorithm (CSFOA). The proposed CSFOA-based feature selection method considered the features by the Bray–Curtis distance. The PD classification is done by the ZF-Net which is trained by proposed CSSFOA to increase the performance of classification. The experimental result reveals that the proposed CSSFOA_ZF-Net algorithm got a better testing accuracy of 0.945, a sensitivity of 0.919, and a specificity of 0.957.

KEYWORDS:

• Gaussian filter
• chronological concept
• smart flower optimization algorithm
• sunflower optimization
• Bray–Curtis distance
• Parkinson's disease
• ZF-Net
• Chronological smart flower optimization Algorithm

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

M. K. Dharani

Dharani M. K. is an Assistant Professor in the Department of Artificial Intelligence, Kongu Engineering College, Tamil Nadu. She is pursuing PhD in the area of Deep learning in medical science. Her other areas of interest are Machine Learning, Networking, Internet of Things. She has published more than 19 papers in international journals and conferences.

R. Thamilselvan

Thamilselvan R. is a professor in the Department of Information Technology, Kongu Engineering College, Tamil Nadu, India. He has completed his M.E Computer Science and Engineering in 2005 and PhD in Computer Science and Engineering in 2013 under Anna University Chennai. He has completed 20 years of teaching service. He has published 15 papers in International Journal, 9 papers in International Conference and 15 papers in National Conference. He has completed one research project sponsored by AICTE, New Delhi under the scheme Research Promotion Scheme (RPS) and organised 2 national level seminar and 1 faculty development programme sponsored by AICTE, New Delhi. His area of interest includes Grid and Cloud Computing, Parallel Processing, Big Data Analytics and Distributed Computing.