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Enhancing Parkinson's disease prediction using meta-heuristic optimized machine learning models.

Afeez A Soladoye1, David B Olawade2,3,4,5, Adebimpe O Esan1

  • 1Department of Computer Engineering, Federal University, Oye-Ekiti, Nigeria.

Personalized Medicine
|July 12, 2025
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Summary

This study enhances Parkinson's disease prediction using meta-heuristic optimization for machine learning models. Optimized models show improved accuracy and efficiency, aiding early detection of this neurological disorder.

Keywords:
Parkinson’s diseasefeature selectionhyperparameter optimizationmachine learningmeta-heuristic algorithms

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Computational Biology

Background:

  • Parkinson's disease (PD) is a progressive neurodegenerative disorder impacting motor and cognitive functions.
  • Early and accurate detection of PD is critical for timely intervention but remains a significant clinical challenge.
  • Traditional diagnostic methods often lack the sensitivity and specificity for early-stage identification.

Purpose of the Study:

  • To enhance the predictive performance of machine learning models for Parkinson's disease detection.
  • To investigate the efficacy of meta-heuristic optimization algorithms in improving model accuracy and efficiency.
  • To identify optimal feature selection and hyperparameter tuning strategies for PD prediction.

Main Methods:

  • Utilized a Parkinson's dataset encompassing demographic, lifestyle, medical, clinical, and cognitive features.
  • Applied three feature selection techniques: Whale Optimization Algorithm (WOA), Artificial Bee Colony Optimization (ABC), and Backward Elimination (BE).
  • Employed Artificial Ant Colony Optimization (ACO) for hyperparameter tuning of Random Forest (RF) models, comparing performance against other ML algorithms.

Main Results:

  • The optimized RF model, combined with Backward Elimination (BE) for feature selection, achieved 93% accuracy and 97% Area Under the Curve (AUC).
  • This optimized model significantly outperformed other evaluated machine learning models, including K-Nearest Neighbors, Support Vector Machines, Logistic Regression, XGBoost, and Stacked Ensemble.
  • Meta-heuristic optimization drastically reduced model tuning time from 133 to 18 minutes, demonstrating enhanced computational efficiency.

Conclusions:

  • Meta-heuristic optimization techniques substantially improve the accuracy and efficiency of machine learning-based Parkinson's disease prediction.
  • The optimized RF model with BE shows significant promise for early and reliable detection of PD.
  • Further clinical validation is necessary to translate these computational findings into practical diagnostic tools.