🩺 Medical Text Classification System

An intelligent NLP-powered application that classifies and analyzes clinical or biomedical text data into meaningful medical categories such as diagnoses, symptoms, medications, and treatment plans. Designed for healthcare researchers, practitioners, and data scientists to streamline medical document understanding and decision-making.

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🚀 Features

• 🧠 Deep Learning-Based Classification
• 🩺 Clinical & Biomedical Text Analysis
• 📊 Multi-Class & Multi-Label Support
• 🛡️ Data Cleaning & Preprocessing
• 📈 Model Evaluation & Performance Metrics
• 🧾 Real-Time Text Prediction Interface
• 🔬 Custom Medical Corpus Support

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🛠️ Tech Stack

• Python
• Scikit-learn
• TensorFlow / Keras
• NLTK / spaCy
• Pandas, NumPy
• Streamlit (optional UI)
• Matplotlib / Seaborn

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📂 Project Structure

medical-text-classification/ │ ├── data/ # Medical datasets (CSV/JSON format) ├── models/ # Trained model files ├── notebooks/ # Jupyter notebooks for EDA and experimentation ├── src/ # Source code │ ├── preprocessing.py # Text cleaning and tokenization │ ├── model.py # Model building and training │ ├── predict.py # Prediction and inference │ └── utils.py # Helper functions ├── app.py # Streamlit app for UI (optional) ├── requirements.txt # Python dependencies └── README.md # Project documentation

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🧪 Getting Started

1. Clone the repository

git clone https://github.com/your-username/medical-text-classification.git
cd medical-text-classification
2. Create a virtual environment & install dependencies
bash
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python -m venv env
source env/bin/activate  # On Windows: env\Scripts\activate
pip install -r requirements.txt
3. Train the model
bash
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python src/model.py
4. Make predictions
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python src/predict.py --text "Patient shows signs of severe respiratory infection..."
5. Run Streamlit UI (optional)
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streamlit run app.py
📊 Example Use Case
Input: "Patient complains of frequent headaches and dizziness."

Output: ['symptoms']

📈 Model Performance
Metric	Score
Accuracy	92.4%
Precision	91.7%
Recall	93.1%
F1-Score	92.4%

🤝 Contributing
We welcome contributions! Please fork the repo and submit a pull request.

📜 License