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NeuroDx: a Novel Machine Learning Paradigm for Unveiling Parkinson’s Disease Patterns

EasyChair Preprint no. 11814

5 pagesDate: January 20, 2024


This paper explores an innovative slant for detecting Parkinson's disease (PD) by analyzing voice data from patients. To extract meaningful features from the MDVP voice input, a machine learning technique, including a Support Vector Machine (SVM) is employed. The study emphasizes the importance of data collection, preprocessing, and feature engineering to improve model accuracy. Robustness is ensured by cross-validation and testing across diverse patient datasets. Integrating voice-based PD detection in clinical practice holds potential for early diagnosis and personalized care. This research highlights the efficacy of voice-based machine learning in enhancing PD detection, offering a non-invasive and patient-centric approach.

Keyphrases: machine learning, Parkinson’s disease, Support Vector Machine

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Sharayu Garad and Pranoti Naiknaware and Anisha Shinde and Ashutosh Garad and Meenakshi Pawar},
  title = {NeuroDx: a Novel Machine Learning Paradigm for Unveiling Parkinson’s Disease Patterns},
  howpublished = {EasyChair Preprint no. 11814},

  year = {EasyChair, 2024}}
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