This project focuses on detecting Parkinson's disease using machine learning models trained on audio features. The study compares the performance of Logistic Regression, Gradient Boosting, Random Forest, and K-Nearest Neighbors (KNN) classifiers to determine the most effective approach for accurate detection.
Features
Preprocessing Audio Data: Extract relevant features from audio recordings.
Training and Evaluation: Compare four ML models on the same dataset.
Performance Metrics: Analyze models based on accuracy, precision, recall, and F1-score.
Flowchart Visualization: A detailed flowchart to outline the process.
Project Structure
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parkinsons-disease-detection/
├── codes/ # Folder containing code for each ML model
│ ├── logistic_regression.py
│ ├── gradient_boosting.py
│ ├── random_forest.py
│ ├── knn.py
├── data/ # Dataset folder (add your audio dataset here)
│ ├── parkinsons_audio_data.csv
├── flowchart.png # Flowchart illustrating the methodology
├── README.md # Project documentation
└── results/ # Folder for saving model results and graphs
├── performance_metrics.csv
├── model_comparison.png
Dataset
The dataset contains audio recordings labeled as Parkinson's and non-Parkinson's.
Audio features include jitter, shimmer, HNR (Harmonic-to-Noise Ratio), and other parameters essential for detecting vocal impairment.
Models Used
Logistic Regression: A simple yet effective classifier for binary problems.
Gradient Boosting: An ensemble method that optimizes classification accuracy through iterative boosting.
Random Forest: A robust ensemble learning method using decision trees.
K-Nearest Neighbors (KNN): A non-parametric algorithm based on proximity.
Workflow
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Preprocessing Extract features from the audio recordings using libraries such as Librosa or Praat. Normalize and split the dataset into training and testing sets.
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Training Train each model using the same dataset and perform hyperparameter tuning for optimal performance.
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Evaluation:
Compare models based on performance metrics such as: Accuracy, Precision, Recall. F1-Score, Use a confusion matrix to visualize predictions. -
Results Summarize results in a tabular format for easy comparison. Visualize performance metrics using bar graphs. How to Run Clone the repository:
bash
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git clone https://github.com/your-username/parkinsons-disease-detection.git
cd parkinsons-disease-detection
Install the necessary libraries:
Run the individual model scripts:
bash
python codes/lr.py
python codes/final.py
python codes/rf.py
python codes/knn.py
Flowchart:
