THYROID DISEASE DETECTION

Abstract

This study investigates the application of machine learning (ML) for early detection of thyroid disease. We 
analyze patient data and thyroid function tests to develop a classification model for categorizing individuals as 
"Negative," "Sick," "Hyperthyroid," or "Hypothyroid". Support Vector Classifier (SVC) and Random Forest 
algorithms are employed for robust analysis. Thyroid disease is a common endocrine disorder characterized by 
dysregulation of hormone production by the thyroid gland. This project focuses on utilizing machine learning 
techniques for the early detection of thyroid disease. Through precise classification, healthcare professionals 
can promptly diagnose and plan treatments tailored to each patient's condition. This approach enables timely 
intervention, personalized care, and improved management of thyroid disorders, ultimately leading to better 
health outcomes for patients.