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https://github.com/mayankyadav23/detection-of-parkinsons-disease
🔍 This repo focuses on detecting Parkinson's Disease using machine learning techniques on vocal features. The project includes data preprocessing, analysis, and model training, achieving a remarkable 99.6% accuracy with the Random Forest Classifier. 🧠
https://github.com/mayankyadav23/detection-of-parkinsons-disease
acmegrade data-science model-building-and-evaluation parkinsons-disease python
Last synced: about 2 months ago
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🔍 This repo focuses on detecting Parkinson's Disease using machine learning techniques on vocal features. The project includes data preprocessing, analysis, and model training, achieving a remarkable 99.6% accuracy with the Random Forest Classifier. 🧠
- Host: GitHub
- URL: https://github.com/mayankyadav23/detection-of-parkinsons-disease
- Owner: mayankyadav23
- Created: 2024-11-05T11:12:40.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-05T13:23:22.000Z (about 2 months ago)
- Last Synced: 2024-11-05T14:24:42.243Z (about 2 months ago)
- Topics: acmegrade, data-science, model-building-and-evaluation, parkinsons-disease, python
- Language: Jupyter Notebook
- Homepage:
- Size: 953 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🧠 Detection of Parkinson's Disease ⚕️
Detecting Parkinson's Disease using machine learning and vocal data analysis.
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## 🧬 About Parkinson's Disease
Parkinson's Disease is a **neurodegenerative disorder** that affects movement and worsens over time. It is primarily characterized by tremors, stiffness, and difficulty with balance and coordination. This condition is caused by the gradual breakdown of dopamine-producing neurons in the brain. Early detection of Parkinson's can significantly improve quality of life by allowing for early intervention and better management of symptoms.
### Key Symptoms:
- Tremors (shaking)
- Rigidity (stiff muscles)
- Bradykinesia (slowness of movement)
- Impaired balance and coordination
*Image Source: Representation of brain areas affected by Parkinson's Disease*
---
## 🌐 Project Summary
This project leverages **Machine Learning** to detect **Parkinson's Disease** by analyzing vocal features. Using a variety of models, we aim to identify patterns in voice data that indicate early symptoms of Parkinson's.
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## 📋 Project Workflow
1. **Data Collection**: Accessed via a reputable source.
2. **Data Preprocessing**: Cleaned, normalized, and prepared for model training.
3. **Exploratory Data Analysis (EDA)**: Uncovered trends and relationships in vocal features.
4. **Balancing & Scaling**: Ensured data is balanced and features are appropriately scaled.
5. **Model Training & Evaluation**: Tested various models to find the most effective one.
---
## 📊 Dataset Details
- **Dataset Used:** Parkinson's Disease Dataset
- **Source:** [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/parkinsons.data)
- **Description:** Contains vocal measurements from individuals to help distinguish between Parkinson's and healthy conditions.
## 🧩 Machine Learning Models
Several algorithms were trained and evaluated to find the best fit for our data:
- **Decision Tree Classifier**
- **Random Forest Classifier**
- **Logistic Regression**
- **Support Vector Machine (SVM)**
- **Naive Bayes**
- **K-Nearest Neighbors (KNN)**
- **XGBoost**
## 🎖️ Best Model Performance
The **Random Forest Classifier** achieved the highest performance:
- **Accuracy:** 99.6%
- **F1 Score:** 0.961
- **R² Score:** 0.862
These metrics reflect the model's effectiveness in detecting Parkinson's indicators from vocal features.
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## 📈 Results & Analysis
| Metric | Random Forest Classifier |
|--------------|--------------------------|
| **Accuracy** | 99.6% |
| **F1 Score** | 0.961 |
| **R² Score** | 0.862 |
**Insights**: The Random Forest model showed superior performance due to its capacity to handle complex data patterns, making it a suitable choice for vocal feature classification.
## 🤝 Contributions
Contributions are welcome! If you’d like to add improvements or suggest enhancements, feel free to create a pull request.
## 📞 Contact Information
For any inquiries or collaboration opportunities, feel free to reach out:
- **Name:** Mayank Yadav
- **Email:** [email protected]
- **LinkedIn:** [LinkedIn Profile](https://www.linkedin.com/in/mayankyadv)
- **GitHub:** [GitHub](https://github.com/mayankyadav23)
I'm always open to discussing new projects, ideas, or opportunities!
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