Thyroid Detection

Thyroid Detection

Overview

This is a professionally designed web-based diagnostic tool for thyroid disorder detection, developed using Python and Streamlit. It uses a trained machine learning model to make quick and accurate predictions based on patient information. The project runs smoothly in VS Code, requires no external database, and comes with all the necessary datasets and scripts included.

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Key Features

• Efficiency: Provides real-time diagnosis output.
• Accuracy: Built on a high-quality dataset and rigorously trained ML model.
• User-Friendly Interface: Built with Streamlit for clean, responsive interaction.
• No Database Required: Uses CSV-based data handling.
• IDE Compatible: Can be run directly from VS Code.

Project Structure

.
├── Streamlit_app.py         # Streamlit web application
├── model.pkl                # Trained and serialized ML model
├── Model_Code.ipynb         # Jupyter Notebook for model development
├── requirements.txt         # Required libraries
├── train_data.csv           # Dataset for training
├── test_data.csv            # Dataset for testing
├── thyroidDF.csv            # Full original dataset
├── Images/
│   ├── IMG_1.png            # Screenshot 1
│   └── IMG_2.png            # Screenshot 2
└── README.md                # Project documentation

Run

To run this project using Visual Studio Code (VS Code):

1. Open VS Code and select the project folder.
2. Ensure Python is installed and selected as your interpreter.
3. Open a new terminal in VS Code.
4. Install required libraries: pip install -r requirements.txt
5. Run the Streamlit application: streamlit run Streamlit_app.py

Attributes Information

• Age: Age of the patient (numeric)
• Sex: Gender (‘M’ or ‘F’)
• On Thyroxine: Patient on thyroxine medication (Yes/No)
• Query on Thyroxine: Patient queries thyroxine use (Yes/No)
• On Antithyroid Meds: On anti-thyroid drugs (Yes/No)
• Sick: Is the patient sick (Yes/No)
• Pregnant: Pregnancy status (Yes/No)
• Thyroid Surgery: Surgery history (Yes/No)
• I131 Treatment: Under I131 radioactive treatment (Yes/No)
• Query Hypothyroid: Suspects hypothyroidism (Yes/No)
• Query Hyperthyroid: Suspects hyperthyroidism (Yes/No)
• Lithium: Taking lithium medication (Yes/No)
• Goitre: Presence of goitre (Yes/No)
• Tumor: Presence of tumor (Yes/No)
• Hypopituitary: Hypopituitarism history (Yes/No)
• Psych: Psychological conditions (Yes/No)
• TSH: Thyroid-stimulating hormone level
• T3: Triiodothyronine level
• TT4: Total thyroxine level
• T4U: Thyroxine utilization rate
• FTI: Free thyroxine index

Available Features

• Real-time AI predictions
• Input-based diagnostic interface
• No need for external database
• Clean and responsive Streamlit UI
• Full source code and model included
• Jupyter Notebook for retraining available
• Runs easily within VS Code

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