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@article{202923,
        author = {Ms. Tejashree A. Ladhe and Dr. Anita V. Nikalje},
        title = {SURVEY OF PARKINSON'S DISEASE DETECTION METHODS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {8933-8939},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202923},
        abstract = {Parkinson's disease (PD) is one of the degenerative diseases that seriously affect people's lives. It occurs when there is death of dopamine producing neurons in the substantia nigra, one of the central nervous systems (CNS) components. People with Parkinson's disease have impairments in their speech, writing, and walking. In recent years, there have been studies that have used gait and even EEG recordings for PD detection. However, the most studied area has been in speech. It has been shown that out of all the PD patients, 90% have been found to have speech impairments. Voice deterioration is one of the most prominent voice changes that occur with worsening PD. Voice analysis is one of the many noninvasive voice treatments that have been shown to improve the quality of life of patients. Therefore, speech analysis has attracted a great deal of attention for the development of tele-monitoring and tele-diagnosis predictive models. The accurate analysis of speech has been one of the most challenging problems in the classification of PD. The primary objective of this paper is to review the application of machine learning and deep learning methods for the classification of Parkinson's disease. The methods of machine learning and deep learning have been employed in the quest for more efficient ways to classify Parkinson's disease (PD). Examples of such classification methods include support vector machines, naive Bayes, deep neural networks, decision trees, and random forests. In reviewing the results of various studies, both machine learning and deep learning algorithms have proven to be very useful and have provided a better means to identify Parkinson's disease in its early stages. The classification accuracy attained by the machine learning classifier. Out of all the deep learning methods, the deep neural network has the highest accuracy of 99.49%. The varioud studies have shown that artificial intelligence has become a powerful tool for learning and has a lot to be offered to both data science and neurology. Overall, the methods of learning have proven to be very useful in solving decision making problems which is particularly true in the area of medical diagnosis.},
        keywords = {Deep neural network, Parkinson’s disease, speech.},
        month = {May},
        }