Citation (MLA Format)

Eroltu, Kaan, and Mahir Yusuf Açan. “A Comparative Study of Machine Learning Algorithms for Parkinson’s Disease Diagnosis.” 3 Apr. 2023.

Abstract

This research presents a comprehensive comparative study of various machine learning algorithms for the diagnosis of Parkinson’s disease. The study evaluates the performance, accuracy, and clinical applicability of different ML approaches in early detection and diagnosis of this neurodegenerative disorder.

Research Overview

Parkinson’s Disease

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by:

  • Motor symptoms (tremor, rigidity, bradykinesia)
  • Non-motor symptoms (cognitive changes, sleep disorders)
  • Dopaminergic neuron degeneration
  • Complex multifactorial etiology

Early Diagnosis Challenge

Early and accurate diagnosis of PD is crucial but challenging due to:

  • Subtle initial symptoms
  • Overlap with other conditions
  • Lack of definitive diagnostic tests
  • Inter-patient variability

Methodology

Machine Learning Algorithms Evaluated

  1. Support Vector Machines (SVM)

    • Linear and non-linear kernels
    • Optimal hyperplane classification

  2. Random Forest

    • Ensemble learning approach
    • Multiple decision trees

  3. Neural Networks

    • Deep learning architectures
    • Multi-layer perceptrons

  4. K-Nearest Neighbors (KNN)

    • Instance-based learning
    • Distance metrics

  5. Gradient Boosting

    • XGBoost implementation
    • Sequential ensemble learning

  6. Logistic Regression

    • Baseline classification model
    • Probabilistic predictions

Dataset & Features

Data Sources:

  • Clinical assessments
  • Voice recordings (vocal biomarkers)
  • Motor function tests
  • Imaging data
  • Patient demographics

Feature Types:

  • Vocal features (jitter, shimmer, pitch)
  • Motor performance metrics
  • Cognitive assessment scores
  • Demographic information

Evaluation Metrics

  • Accuracy: Overall correctness
  • Sensitivity: True positive rate
  • Specificity: True negative rate
  • Precision: Positive predictive value
  • F1-Score: Harmonic mean of precision and recall
  • ROC-AUC: Area under receiver operating characteristic curve

Key Findings

Algorithm Performance Comparison

Best Performing Algorithms:

  1. Random Forest: High accuracy and robustness
  2. Gradient Boosting (XGBoost): Superior prediction capability
  3. Neural Networks: Complex pattern recognition

Key Results:

  • ML algorithms achieve >85% accuracy in PD diagnosis
  • Ensemble methods outperform single classifiers
  • Vocal features are highly discriminative
  • Combining multiple feature types improves accuracy

Feature Importance

Most Predictive Features:

  • Vocal biomarkers (jitter, shimmer)
  • Motor function scores
  • Age and gender
  • Specific gait parameters

Clinical Implications

Early Detection

Machine learning enables:

  • Pre-symptomatic risk assessment
  • Earlier intervention opportunities
  • Objective diagnostic support
  • Reduced diagnostic delays

Personalized Medicine

AI-driven diagnosis supports:

  • Individual risk profiling
  • Treatment response prediction
  • Disease progression monitoring
  • Personalized treatment plans

Healthcare System Benefits

  • Reduced diagnostic costs
  • Improved diagnostic accuracy
  • Scalable screening tools
  • Remote monitoring capabilities

Technical Contributions

Model Optimization

  • Hyperparameter tuning strategies
  • Cross-validation techniques
  • Feature selection methods
  • Handling class imbalance

Implementation

  • Python-based ML pipeline
  • Scikit-learn and TensorFlow frameworks
  • Data preprocessing and normalization
  • Model validation protocols

Research Impact

This comparative study provides:

  • Evidence-based algorithm selection guidance
  • Performance benchmarks for PD diagnosis
  • Insights into feature engineering
  • Foundation for clinical AI tools

Future Directions

  1. Multimodal Data Integration

    • Combining imaging, genetic, and clinical data
    • Deep learning for complex pattern recognition

  2. Longitudinal Studies

    • Tracking disease progression
    • Predicting treatment outcomes

  3. Real-World Deployment

    • Clinical validation studies
    • User-friendly diagnostic tools

  4. Explainable AI

    • Interpretable models for clinical use
    • Understanding prediction mechanisms

Technologies Used

  • Programming: Python
  • ML Libraries: scikit-learn, TensorFlow, XGBoost
  • Data Analysis: pandas, numpy
  • Visualization: matplotlib, seaborn
  • Statistical Analysis: scipy, statsmodels

Keywords

Machine Learning, Parkinson’s Disease, Diagnosis, Healthcare AI, Comparative Analysis, Neurodegenerative Diseases, Support Vector Machine, Random Forest, Neural Networks, Deep Learning, Medical AI

Author Information

Kaan Eroltu
Primary Author

Mahir Yusuf Açan
Co-author
Dentistry Student & Data Scientist
Üsküdar University

Publication Date

April 3, 2023

Acknowledgments

This research demonstrates the potential of machine learning in revolutionizing neurological disease diagnosis and improving patient outcomes through early detection.

Contact

For more information about this research:
📧 mahiryusuf531@gmail.com
🔬 ORCID: 0000-0003-0540-7099
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