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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Voice Based Parkinson's Disease Detection using ML |
| Authors Name: | Muskan Bahrani , Kaustubh Kharche , Meet Chhabria , Sakshi Shinde |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_216803
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| Published Paper Id: | IJNRD2404409 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | Parkinson's disease (PD) is a complex neurological disorder that progressively affects an individual's motor functions and, to a varying extent, their cognitive abilities. As a result, individuals with PD experience a range of symptoms such as tremors, bradykinesia (slowness of movement), rigidity, and postural instability. Additionally, non-motor symptoms including cognitive impairment, mood disorders, and autonomic dysfunction further contribute to the multifaceted nature of PD. All these symptoms start showing at the very later stage of the disease. Hence there is no early detection and the disease becomes more serious.[6] Recognizing the significance of early intervention in improving the quality of life for individuals with PD, our project aims to develop an advanced prediction model for the timely detection of Parkinson's disease. The utilization of machine learning algorithms and data analysis techniques on a comprehensive dataset is crucial for achieving a precise and reliable predictive model.[1] This dataset may include a variety of voice-based information enabling a holistic understanding of the disease. The predictive model seeks to identify patterns and correlations within the data that are indicative of early-stage Parkinson's disease, allowing for intervention before the onset of severe symptoms. Early detection holds the potential to facilitate timely and personalized treatment strategies, potentially slowing the progression of the disease and enhancing overall patient outcomes. |
| Keywords: | Parkinson’s Disease, Machine Learning, Data Analysis, Mel-Frequency Cepstral Coefficient (MFCC). |
| Cite Article: | "Voice Based Parkinson's Disease Detection using ML", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.e66-e71, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404409.pdf |
| Downloads: | 00034 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404409 Registration ID: 216803 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: e66-e71 Country: Ulhasnagar 4, Maharashtra, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404409 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404409 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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