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A Novel Artificial-Intelligence-Based Approach for Classification of Parkinson's Disease Using Complex and Large

Rahul Nijhawan1, Mukul Kumar2, Sahitya Arya3

  • 1Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, India.

Biomimetics (Basel, Switzerland)
|August 25, 2023
PubMed
Summary

This study introduces an AI model using voice analysis to detect Parkinson's disease (PD), outperforming existing methods. The transformer-based approach offers a more accurate and efficient system for early PD identification.

Keywords:
Parkinson’s diseasedysphonia measuresneural networktabular datatransformerunbalanced class

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Speech Science

Background:

  • Parkinson's disease (PD) is a neurodegenerative disorder affecting motor function, with prevalence increasing in aging populations.
  • Early detection of PD is crucial for managing symptoms and improving patient outcomes.
  • Current diagnostic methods can be invasive or lack sensitivity for early-stage detection.

Purpose of the Study:

  • To develop an accurate Artificial Intelligence (AI) model for early Parkinson's disease detection using voice recordings.
  • To investigate the efficacy of a transformer-based neural network for analyzing vocal biomarkers of PD.
  • To compare the proposed AI model's performance against state-of-the-art methods.

Main Methods:

  • A transformer-based neural network was developed to analyze dysphonia measures extracted from voice recordings.
  • An XGBoost-based feature selection method and a fully connected neural network layer were employed for continuous dysphonia measures.
  • The proposed model's performance was evaluated against conventional machine learning techniques (MLP, SVM, Random Forest) and Gradient-Boosted Decision Trees (GBDTs).

Main Results:

  • The transformer-based AI model achieved superior performance compared to state-of-the-art methods, including GBDTs, with at least a 1% improvement in Area Under the Curve (AUC).
  • The model demonstrated enhanced precision and recall scores for Parkinson's disease detection.
  • The study highlighted the resilience of transformer networks to increased depth compared to simpler Multi-Layer Perceptron (MLP) networks.

Conclusions:

  • The developed transformer-based AI model offers a precise and efficient system for early Parkinson's disease detection through voice analysis.
  • Neural network-based approaches are advantageous for analyzing vocal characteristics and hold potential for multimodal diagnostic solutions.
  • This research advances the application of deep learning in analyzing dysphonia measures for improved neurological disorder diagnosis.