Parkinson’s Disease Detection Using Spiral Images and Voice Data Set

Authors

  • Janapala Ramani Sreenija UG – CSE (AI&ML) Engineering, Sphoorthy Engineering College, JNTUH, Hyderabad, Telangana, India. Author
  • Kothakapu Monu Reddy UG – CSE (AI&ML) Engineering, Sphoorthy Engineering College, JNTUH, Hyderabad, Telangana, India. Author
  • Yalla Geetha Madhuri UG – CSE (AI&ML) Engineering, Sphoorthy Engineering College, JNTUH, Hyderabad, Telangana, India. Author
  • Yelakonda Akshara UG – CSE (AI&ML) Engineering, Sphoorthy Engineering College, JNTUH, Hyderabad, Telangana, India. Author
  • Dr. M. Ramesh Professor & Head of the Department, Department of Computer Science & Engineering (AI&ML), Sphoorthy Engineering College, JNTUH, Hyderabad, Telangana, India. Author

DOI:

https://doi.org/10.47392/IRJAEH.2025.0315

Keywords:

Voice Feature Extraction, Spiral Drawing Classification, Random Forest Classifier, Convolutional Neural Network (CNN), Non-Invasive Diagnosis, Parkinson’s Disease Detection, Streamlit Web Application, Dual-Modal Analysis

Abstract

Parkinson’s Disease (PD) is a degenerative neurological disorder that significantly affects motor functions, vocal clarity, and overall coordination. The accurate and early detection of PD is critical for managing symptoms and improving patient outcomes. This base paper presents a dual-modal diagnostic approach that utilizes both voice feature analysis and spiral drawing interpretation to detect Parkinson’s Disease. Voice data is processed to extract features such as jitter, shimmer, and harmonic-to-noise ratio, which are then analyzed using a Random Forest classifier. Parallelly, spiral drawings — commonly used in clinical motor assessments — are classified using a Convolutional Neural Network (CNN) to detect tremor patterns. A web-based application is developed using Streamlit, allowing users to upload data and receive real-time predictions. The integration of two distinct data modalities enhances diagnostic accuracy and provides a non-invasive, cost-effective alternative to traditional methods. Experimental results validate the effectiveness of both models independently, demonstrating that a combined system can provide a more reliable screening tool. This project highlights the potential of AI-powered healthcare tools in supporting early-stage Parkinson’s diagnosis, particularly in remote or resource-limited settings.

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Published

2025-05-13

How to Cite

Parkinson’s Disease Detection Using Spiral Images and Voice Data Set. (2025). International Research Journal on Advanced Engineering Hub (IRJAEH), 3(05), 2148-2155. https://doi.org/10.47392/IRJAEH.2025.0315

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