Thyroid Disease Prediction: Leveraging Machine Learning for Accuracy

Abstract

Millions of people worldwide suffer from thyroid conditions like hyperthyroidism and hypothyroidism, which, if left untreated, might have serious health repercussions. For efficient management and lower healthcare costs, early detection and accurate prognosis are essential. This research uses cutting-edge machine learning techniques to present a comprehensive method for predicting thyroid dysfunction. Using a range of techniques, including random forests, logistic regression, and support vector machines, decision trees, we examine a heterogeneous dataset comprising clinical and biochemical variables. Strict feature selection methods are used to identify the most important variables, improving prediction accuracy. Our thorough analysis shows that sophisticated machine learning models can greatly enhance patient outcomes and early diagnosis in the treatment of thyroid disorders.