Improving Thyroid Disease Diagnosis Through Machine Learning and Data Analysis

Abstract

Thyroid disorders are among the most prevalent endocrine disorders, impacting a significant portion of the global population. Many people in the world are suffering from thyroid disorder. Early detection and treatment of thyroid disease is essential to prevent complications. Traditional methods such as thyroid function tests and thyroid scans can be time-consuming and expensive. Machine learning is a rapidly developing field that has the ability to improve the early detection and diagnosis of thyroid disease. The fusion of machine learning methodologies with medical diagnostics has emerged as a promising avenue for enhancing thyroid disorder detection. ML algorithms, including decision trees, random forests, support vector machines, gradient boosting, and adaptive boosting have been extensively employed in the creation of thyroid illness prediction models. In this research comparative analysis of various ML approaches applied to thyroid detection are reviewed. The suggested system employs a variety of ML approaches to improve illness prediction accuracy through comparative investigation.