Thyroid Disease Detection using Machine Learning Models and Application Development

Thyroid disorders affect many individuals and demand swift detection as well as proper diagnosis to stop medical complications from arising. A machine learning detection system for thyroid conditions will be developed using patient data attributes according to this research. The analysis relied on a complete dataset that contained features involving patient age and sex together with thyroid-related medical history along with clinical measurements. The examination utilized Random Forest together with Gradient Boosting and Neural Network algorithms to which hyperparameter optimalization techniques were applied. The Neural Network exhibited superior accuracy among the models so it became the basis for developing a user-friendly web application through Streamlit. Users can submit patient information to this system which predicts the risk of thyroid disease with a practical solution for medical practitioners available to them.