  Parkinson's disease is a widespread neurodegenerative condition necessitating
early diagnosis for effective intervention. This paper introduces an innovative
method for diagnosing Parkinson's disease through the analysis of human EEG
signals, employing a Support Vector Machine (SVM) classification model. this
research presents novel contributions to enhance diagnostic accuracy and
reliability. Our approach incorporates a comprehensive review of EEG signal
analysis techniques and machine learning methods. Drawing from recent studies,
we have engineered an advanced SVM-based model optimized for Parkinson's
disease diagnosis. Utilizing cutting-edge feature engineering, extensive
hyperparameter tuning, and kernel selection, our method achieves not only
heightened diagnostic accuracy but also emphasizes model interpretability,
catering to both clinicians and researchers. Moreover, ethical concerns in
healthcare machine learning, such as data privacy and biases, are
conscientiously addressed. We assess our method's performance through
experiments on a diverse dataset comprising EEG recordings from Parkinson's
disease patients and healthy controls, demonstrating significantly improved
diagnostic accuracy compared to conventional techniques. In conclusion, this
paper introduces an innovative SVM-based approach for diagnosing Parkinson's
disease from human EEG signals. Building upon the IEEE framework and previous
research, its novelty lies in the capacity to enhance diagnostic accuracy while
upholding interpretability and ethical considerations for practical healthcare
applications. These advances promise to revolutionize early Parkinson's disease
detection and management, ultimately contributing to enhanced patient outcomes
and quality of life.
