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Automated Diagnosis of Parkinson's Disease Using Voice Analysis
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- Project Mart
Introduction
Parkinson's Disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms, including vocal impairments that are often imperceptible to the human ear. This project proposes the development of an automated diagnostic system using voice analysis to detect PD. By employing machine learning algorithms, the system aims to identify subtle changes in speech patterns that are indicative of PD, offering a cost-effective and accessible diagnostic alternative.
Background
Recent advancements in machine learning have facilitated the development of systems capable of analyzing voice signals to detect diseases like PD. Studies have shown that voice impairments are prevalent among PD patients, and these can be detected through acoustic analysis. Various machine learning models, such as Support Vector Machines (SVM), Random Forests (RF), and neural networks, have been applied successfully to classify voice recordings into healthy and PD categories.
Project Objective
The primary objective of this project is to create an intelligent system that can automatically analyze voice recordings to diagnose PD. The system will utilize advanced feature extraction techniques and machine learning models to enhance classification accuracy.
Methodology
1. Data Collection and Preprocessing
- Dataset: Utilize the publicly available dataset from the UCI Machine Learning Repository, which contains voice recordings from individuals with and without PD.
- Feature Extraction: Extract relevant acoustic features such as Mel-frequency cepstral coefficients (MFCCs) and other phonation-related features.
2. Model Development
- Machine Learning Models: Implement various models including SVM, Random Forest, and neural networks for classification tasks.
- Dimensionality Reduction: Apply techniques like Linear Discriminant Analysis (LDA) for dimensionality reduction to improve model performance.
3. Training and Evaluation
- Training: Use techniques like Synthetic Minority Over-sampling Technique (SMOTE) for handling imbalanced datasets.
- Validation: Employ Leave-One-Subject-Out (LOSO) cross-validation to ensure robust model evaluation.
- Evaluation Metrics: Assess model performance using accuracy, sensitivity, specificity, and F1-score.
Expected Outcomes
The proposed system is expected to achieve high accuracy in diagnosing PD from voice recordings. By leveraging machine learning techniques, the system should provide a reliable and non-invasive diagnostic tool that can be used in clinical settings.
Conclusion
This project seeks to advance the field of automated disease diagnosis by developing a system capable of detecting Parkinson’s Disease through voice analysis. The integration of machine learning models with advanced feature extraction is anticipated to enhance diagnostic accuracy significantly.
For further details on related research, please refer to the paper "Automated Detection of Parkinson’s Disease Based on Multiple Voice Features" available at https://www.frontiersin.org/articles/10.3389/fneur.2019.00369/full.
Dataset link: UCI Machine Learning Repository - Parkinson's Dataset.