Thyroid disease a very common problem in India, more than one crore people are suffering with the disease every year. Thyroid disorder can speed up or slow down the metabolism of the body.

The main objective of this project is to predict if a person is having hypothyroid, hyperthyroid, or negative (no thyroid) with the help of Machine Learning. Classification algorithms such as Logistic regression, Random Forest, Decision Tree, Naïve Bayes, Support Vector Machine have been trained on the thyroid dataset, UCI Machine Learning repository. Random Forest performed well with better accuracy (96.6%), precision and recall. After that, application has deployed on Heroku with the help of flask.

https://thyroid-classification.herokuapp.com/

https://github.com/ahmadtaquee/thyroid-classification-end_to_end-deployment/blob/main/Docs/Thyroid-Disease-Detection-Demo.mp4

• Python 3.7
• Front-end: HTML, CSS, Bootstrap
• Back-end: Flask framework
• IDE: Jupyter Notebook, PyCharm
• Database: MongoDB Atlas
• Deployment: Heroku

Code is written in Python 3.7. If you don't have python installed on your system, click here https://www.python.org/downloads/ to install.

• Create virtual environment: conda create -n myenv python=3.7
• Activate the environment: conda activate myenv
• Install the packages: pip install -r requirements.txt
• Run the app: python main.py

The dataset is collected from UCI Machine Learning Repository, "Thyroid Disease Detection"

• Various classification algorithms like Logistic Regression, Random Forest, Decision Tree, Naïve Bayes, Support Vector Machine tested.
• Random Forest, Decision Tree and Logistic regression were given better results. Random Forest was chosen for the final model training and testing.
• Model performance evaluated based on accuracy, confusion matrix, classification report.

MongoDB Atlas database used for this project

The final model is deployed on Heroku using Flask framework