Ai-Based Early Detection Of Thyroid Disease And Stage Estimation System Using Xgboost

Abstract

Thyroid disorders affect millions worldwide, with conditions like hypothyroidism and hyperthyroidism disrupting metabolism, cardiovascular function, and quality of life. Conventional diagnosis relies on clinical evaluation and laboratory tests but often faces challenges such as subjectivity, overlooked mild cases, and lack of efficiency in large-scale screening. The proposed study will address these issues by considering an Advanced Thyroid Disease Detection system, which will use Extreme Gradient Boosting (XGBoost) with Explainable AI (SHAP) to make transparent and accurate predictions. Unlike the traditional ones, the system not only groups the disorders of the thyroid but also predicts the levels of the hormone as percentages, states of severity of the diseases, and presents the outcomes in a friendly graphical interface. By integrating accuracy, usability and interpretability, there is greater care regarding the accuracy of the diagnosis, early detection, and physician trust, which subsequently develops clever and scalable healthcare solutions.