Parkinson’s Disease Detection Using Machine Learning

Authors

  • CH. Lalitha Haripriya B. Tech Students, Department of Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India Author
  • CH. Sirisha B. Tech Students, Department of Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India Author
  • A. Gnaneswar B. Tech Students, Department of Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India Author
  • K. Nagendra B. Tech Students, Department of Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India Author
  • Dr. R. Rajender Professor & Hod, Department of Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India Author

DOI:

https://doi.org/10.32628/CSEIT2511142

Keywords:

Random Forest, Support Vector Machines, Feature Extraction, Clinical Data, Early Detection

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting movement and motor control. Early detection is crucial for effective treatment and management. This paper presents a machine learning-based approach to detect Parkinson’s disease using speech and biomedical data. The proposed model utilizes various machine learning algorithms, including Support Vector Machines (SVM), Random Forest, and Deep Learning techniques, to classify PD and non-PD subjects. The model is trained on a publicly available dataset and achieves significant accuracy in classification. Multi-modal analysis enhances diagnostic accuracy, offering a non-invasive, cost-effective solution. Future work will focus on real-time monitoring, expanding datasets, integrating wearable technology, and improving model interpretability for clinical applications.

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References

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Published

23-02-2025

Issue

Section

Research Articles

How to Cite

Parkinson’s Disease Detection Using Machine Learning. (2025). International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 3150-3154. https://doi.org/10.32628/CSEIT2511142