<![CDATA[Thyroid illness is one of the most prevalent medical problems that has a direct impact on a person's physical and emotional well-being. The 2017–2020 NHANES data, which is extensive and contains a wide variety of 6,992 people and XX characteristics, is the source of the ML used in this study. Improving the early identification and classification of vulnerable people is the goal of this study. The machine learning techniques used in this study include K-Nearest Neighbor (KNN), Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR), Extreme Gradient Boosting (EGB), LightGBM (LGBM), Multi-Layer Perceptron (MLP), and Gradient Boosting. Evaluation of these algorithms revealed that RF, EGB, and LGBM exhibited exceptional accuracy, reaching an impressive 0.90. Among them, RF demonstrated the highest precision at 0.98, showcasing its ability to correctly identify individuals at risk with a high degree of confidence. Moreover, the study identified KNN as the algorithm with the highest recall value, reaching 0.73, highlighting its effectiveness in capturing a substantial proportion of true positive cases. EGB emerged with the highest F1-Score, shows a proportionate balance between recall and accuracy. Additionally, EGB displayed the highest Area Under the Curve (AUC) at 0.82, underscoring its robust predictive capabilities. This research underscores the pivotal role of ML algorithms in predicting and classifying thyroid disease risk, offering valuable insights for early intervention and personalized healthcare strategies. The high accuracy, precision, and recall values observed with RF, EGB, and LGBM suggest their potential as powerful tools for improving diagnostic capabilities in the realm of thyroid disease, contributing to more effective and timely patient care. As advancements in machine learning continue, the integration of these techniques into healthcare frameworks holds promise for enhancing our understanding and management of thyroid disorders.]]>
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