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June 15, 2026Scientific ReportsOpen Access

Enhancing prediction accuracy for Parkinson’s disease using advanced machine learning models

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Authors

PSPradeepta Kumar SarangiChitkara UniversityRSRajnish SrivastavaChitkara UniversityMDMonica DuttaGLA University

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Overview

Randomized trial enhances prediction accuracy for Parkinson's disease, indicating effective detection methods.

Key Points

  • The aim is to improve detection accuracy for Parkinson's disease using advanced machine learning algorithms.
  • Utilized acoustical data from the UCI Parkinson’s disease dataset
  • Compared four machine learning algorithms: XGBoost, Random Forest, SVM, and KNN
  • Implemented 10-Fold cross-validation, SMOTE for balancing, and PCA for dimensionality reduction
  • XGBoost achieved the highest accuracy at 97.22%, outperforming other models in classifying Parkinson’s disease
  • SMOTE significantly enhanced the performance of KNN and other ML algorithms
  • PCA reduced dimensionality while retaining essential information, showing similar performance across all algorithms after its application.

Cite This Study

Sarangi et al. (2026) studied this question.

synapsesocial.com/papers/6a2f982ba1cfeec4908293e9https://doi.org/10.1038/s41598-026-54057-3
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