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Classification Of Parkinson's Disease Using Machine Learning Technique

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Research demonstrates machine learning improves diagnosis in parkinson's disease, suggesting essential benefits in resource-limited settings.

The CNN model achieved a correctness rate of 94.3%, showing superior performance over other diagnostic techniques.
Analysis of tremor frequency and voice pitch variation emerged as the most effective features for identifying parkinson's disease.
AI-driven technologies can significantly reduce diagnostic delays in parkinson's disease, enhancing early detection capabilities.
The study relies on datasets from public domains and local hospitals to create a robust machine learning diagnosis system.

Wissam Abbas Hadi (2025) studied this question.

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View Full Paper

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Authors

Research demonstrates machine learning improves diagnosis in parkinson's disease, suggesting essential benefits in resource-limited settings.

The CNN model achieved a correctness rate of 94.3%, showing superior performance over other diagnostic techniques.
Analysis of tremor frequency and voice pitch variation emerged as the most effective features for identifying parkinson's disease.
AI-driven technologies can significantly reduce diagnostic delays in parkinson's disease, enhancing early detection capabilities.
The study relies on datasets from public domains and local hospitals to create a robust machine learning diagnosis system.

Wissam Abbas Hadi (2025) studied this question.

View Full Paper

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Bookmark