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Parkinson disease severity detection based On OPtFuzNet with fused features
1Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, Tamil Nadu, India.
Summary
This study introduces an optimized framework using gait analysis and an Optimized Fuzzy Neural Network (OPtFuzNet) to detect Parkinson's disease severity. The model achieved high accuracy, offering potential for early diagnosis and improved patient mobility management.
Area of Science:
- Biomedical Engineering
- Machine Learning
- Neurology
Background:
- Parkinson's disease (PD) is a progressive neurodegenerative disorder impacting millions globally, characterized by severe mobility impairment.
- Early and accurate determination of PD severity is crucial for developing effective treatment strategies.
Purpose of the Study:
- To develop an optimized feature-fusion framework for detecting Parkinson's disease severity using gait data.
- To enhance early diagnosis and clinical interpretability through advanced computational methods.
Main Methods:
- A novel feature-fusion framework employing a median filter for pre-processing, SAE and IDCN for local/global feature extraction, and an Optimized Fuzzy Neural Network (OPtFuzNet) for classification.
- Parameter optimization for OPtFuzNet was achieved using an Improved Grey Wolf Optimizer with Lévy flight (IGWO-Lévy).
Main Results:
- The proposed OPtFuzNet model demonstrated superior performance on benchmark datasets (GAIT-IT and GAIT-IST), achieving accuracies of 98.08% and 98.12%, respectively.
- Feature importance analysis identified key gait characteristics, enhancing the model's clinical interpretability.
Conclusions:
- The study validates the efficacy of the proposed feature fusion and optimization strategy for identifying gait patterns indicative of Parkinson's disease severity.
- Further validation using real-world clinical data is recommended for practical application, as current datasets are based on simulated gait patterns.
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