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September 10, 2025Healthcare Informatics ResearchOpen Access

Advancements in Parkinson’s Disease Prediction Using Machine Learning: A Neurological Perspective

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Authors

ACAravalli Sainath ChaithanyaNKN. Kiran KumarGPGugulothu Venkatesh Prasad

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Overview

This analysis demonstrates machine learning effectively predicts severity in parkinson's disease, suggesting new diagnostic strategies.

Key Points

  • The phase-shift ensembling model achieved a notable predictive performance with an sMAPE of 55.
  • Integrating multiple data types led to enhanced prediction accuracy, particularly for motor function assessment.
  • Utilizing random forest regression, predictions showed an sMAPE of 77.32 for motor function severity.
  • The study indicates a robust method for modeling disease progression using machine learning applications.

Cite This Study

Chaithanya et al. (2025) studied this question.

synapsesocial.com/papers/68c1c9d254b1d3bfb60f2bd1https://doi.org/10.4258/hir.2025.31.3.274
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