🩺 Track_ur_Health

An AI-powered disease prediction system that analyzes symptoms to provide potential diagnoses, treatment options, and medical recommendations.

✨ Features

• Symptom Analysis: Advanced parsing and processing of user-reported symptoms
• Predictive Diagnostics: AI-based prediction of potential diseases based on symptom patterns
• Treatment Recommendations: Suggested treatment options based on predicted conditions
• Specialist Referrals: Recommendations for appropriate medical specialists
• Risk Assessment: Evaluation of condition severity and risk levels
• Preventive Measures: Customized prevention tips for identified conditions

🔧 Installation

1. Clone the repository

   git clone https://github.com/yourusername/Track_ur_Health.git
   cd Track_ur_Health

2. Create a virtual environment (recommended)

   python -m venv venv
   
   # On Windows
   venv\Scripts\activate
   
   # On macOS/Linux
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

💻 Usage

Run the web interface

python main.py

The Gradio interface will be available at link generated

You'll see an interactive interface where you can:

• Enter symptoms separated by commas
• Adjust the number of results you want to see
• Get predictions along with medical recommendations

📊 Dataset Format

The repository includes a dataset.csv file ready to use. If you want to use your own dataset, ensure it has the following columns:

Column Name	Description	Example
disease	Name of the medical condition	Influenza
symptoms	Comma-separated list of symptoms	fever, cough, sore throat, runny nose
cures	Comma-separated list of treatments	rest, fluids, antiviral medication
doctor	Medical specialists to consult	General Practitioner, Infectious Disease Specialist
risk level	Severity of the condition	moderate

🧠 Model Details

Machine Learning Pipeline

• Preprocessing: Advanced text cleaning, lemmatization, and medical term preservation
• Data Augmentation: Oversampling with variations to improve generalization
• Feature Engineering: TF-IDF vectorization with n-grams and symptom-disease co-occurrence weighting
• Model: Optimized Random Forest classifier with class balancing
• Performance: Evaluated using stratified cross-validation

Key Technical Features

• Automatic model caching for improved performance
• Advanced symptom parsing and normalization
• Disease grouping for improved prediction accuracy
• Risk level assessment
• Preventive measure generation

Pull Request Process

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📸 Screenshots

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Made with ❤️ for better healthcare