Enhanced Thyroid Disease Classification using Machine Learning with Cross-Validation Analysis

Millions of people worldwide, irrespective of age or gender, suffer from thyroid abnormalities, which are among the most prevalent endocrine diseases. By secreting hormones like thyroxine (T4) and triiodothyronine (T3), the thyroid gland, which is situated in the neck, is essential in controlling a number of metabolic processes. Numerous body processes, such as metabolism, heart rate, temperature regulation, and energy production, are impacted by these hormones. Conditions include goiter, thyroid nodules, hypothyroidism (underactive thyroid), hyperthyroidism (overactive thyroid), and thyroid cancer can arise from any interference with this gland's normal function. Such disorders can go misdiagnosed for extended periods of time due to their sometimes vague and subtle symptoms, which, if left untreated, might result in problems. Using patient health data, this study explores the use of machine learning (ML) techniques to the thyroid illness detection process. In particular, this study investigates three categorization algorithms: Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbors (KNN). A publicly accessible thyroid illness dataset with a variety of clinical characteristics was used to train these models.