COMPARATIVE ANALYSIS OF STATE-OF-THE-ART CLASSIFIERS FOR PARKINSON'S DISEASE DIAGNOSIS

Authors

  • Ahmed Alaa Hani Duhok Polytechnic University
  • Amira Bibo Sallow Duhok Polytechnic University
  • Hawar Bahzad Ahmad Nawroz University
  • Saman Mohammed Abdulrahman Akre University for Applied Sciences
  • Renas Rajab Asaad Nawroz University
  • Subhi R. M. Zeebaree Duhok Polytechnic University
  • Dilovan Asaad Majeed Akre University for Applied Sciences

DOI:

https://doi.org/10.22437/jiituj.v8i2.32771

Keywords:

Artificial Intelligence, Machine Learning, Parkinson’s Disease, Gradient Boosting, XGBoost

Abstract

Parkinson's disease (PD) presents a growing global health challenge, with early detection being crucial for effective management and treatment. This study seeks to develop an innovative machine learning (ML) framework for the early detection of PD by integrating advanced techniques for data preprocessing, dimensionality reduction, feature selection, and ensemble classification, aiming to significantly improve detection accuracy and timeliness. The research employs a robust ML pipeline, beginning with data preprocessing using mean imputation, standardization, min-max scaling, and SMOTE (Synthetic Minority Over-sampling Technique) to handle imbalanced data. Dimensionality reduction is achieved through Principal Component Analysis (PCA), while feature selection is performed using SelectKBest coupled with the ANOVA F-test to identify the most relevant features. Four ensemble methods—Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine (SVM)—are evaluated for classification. Among the classifiers tested, the Gradient Boosting model stands out with an impressive accuracy of 0.9487, demonstrating its superior performance in PD detection. Integrating multiple preprocessing, dimensionality reduction, and feature selection techniques proves essential in optimizing model performance, highlighting the importance of a multifaceted approach in handling complex datasets. This research introduces a comprehensive ML framework that combines multiple advanced techniques in a streamlined process, significantly improving the early detection of Parkinson's disease. Ensemble methods, combined with strategic feature selection and data balancing techniques, offer a novel approach that could be applied to other neurodegenerative disorders, expanding its potential impact beyond PD detection.

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Published

2024-09-23

How to Cite

Hani, A. A., Sallow, A. B., Ahmad, H. B., Abdulrahman, S. M., Asaad, R. R., Zeebaree, S. R. M., & Majeed, D. A. (2024). COMPARATIVE ANALYSIS OF STATE-OF-THE-ART CLASSIFIERS FOR PARKINSON’S DISEASE DIAGNOSIS. Jurnal Ilmiah Ilmu Terapan Universitas Jambi, 8(2), 409-423. https://doi.org/10.22437/jiituj.v8i2.32771

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