Abstract
Parkinson's Disorder (PD) is a progressive neurodegenerative disorder, which is incurable. Diagnosis of PD at an early stage aids in the delay of the progression. It requires an accurate and robust system to provide early diagnosis of PD. Machine Learning (ML) based techniques help in developing of PD diagnosis system. This study presents a complete review of the various machine learning techniques along with their working principle that helps for the development of PD diagnosis system. This research highlights the summary of methodologies and also presents a generic framework for the PD diagnosis based on voice signals.
References
- J.Jankovic, Parkinson’s disease: clinical features and diagnosis, J. Neurol.Neurosurg. Psychiatry 79 (4) (2008) 368–376.
- C. Okan Sakar, Gorkem Serbes et al, “A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform”, Applied Soft Computing Journal 74 (2019) 255–263.
- H.Gürüler, A novel diagnosis system for Parkinson’s disease using complex-valued artificial neural network with k-means clustering feature weighting method, NeuralComput.Appl.28(2016)
- Arvind Kumar Tiwari “Machine learning based approaches for prediction of parkinson’s disease”, Machine Learning and Applications: An International Journal (MLAIJ) Vol.3, No.2, June 2016.
- Sakara, Batalu. E., & Kursunb, (2014)O. Telemonitoring of changes of unified Parkinson’s disease rating scale using severity of voice symptoms.
- Rustempasic, Indira, & Can, M. (2013). Diagnosis of Parkinson’s Disease using Fuzzy C-Means Clustering and Pattern Recognition. SouthEast Europe Journal of Soft Computing, 2(1).
- Shahbakhi, M., Far, D. T., & Tahami, E. (2014). Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine. Journal of Biomedical Science and Engineering, 2014.
- Sellam, V., & Jagadeesan, J. (2014). Classification of Normal and Pathological Voice Using SVM and RBFNN. Journal of Signal and Information Processing, 2014.
- Ma, C., Ouyang, J., Chen, H. L., & Zhao, X.H. (2014). An Efficient Diagnosis System for Parkinson’s Disease Using Kernel Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach. Computational and mathematical methods in medicine, 2014.
- Yahia A, Laiali A. (2014). Detection of Parkinson Disease through Voice Signal Features. Journal of American Science 2014; 10(10), 44-47.
- Nivedita C., Yogender A., R. K. Sinha (2013). Artificial Neural Network based Classification of Neurodegenerative Diseases. Advances in Biomedical Engineering Research (ABER) Volume 1 Issue 1.
- Farhad S. G., Peyman M. (2013). A Case Study of Parkinson’s disease Diagnosis using Artificial Neural Networks. International Journal of Computer Applications (0975 – 8887) Volume 73– No.19
- H. S. Pal, A. Kumar and A. Vishwakarma, "TQWT based Electrocardiogram Compression using Optimized Thresholding," 2021 Advanced Communication Technologies and Signal Processing (ACTS), Rourkela, India, 2021, pp. 1-5, doi: 10.1109/ACTS53447.2021.9708289.
- Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.
- API design for machine learning software: experiences from the scikit-learn project, Buitinck et al., 2013.
