Automated Thyroid Disease Prediction Using AutoGluon: An Efficient Machine Learning Approach

This study investigates AutoGluon, an advanced AutoML framework, for automated prediction of thyroid disorders (hypothyroidism, hyperthyroidism, and thyroid cancer). Unlike traditional machine learning approaches that depend heavily on manual configuration, AutoGluon automates essential processes including model selection, data preprocessing, and hyperparameter optimization. Evaluated on a thyroid disease dataset, AutoGluon demonstrated superior performance compared to conventional models (logistic regression, random forests, and support vector machines), achieving higher accuracy, precision, recall, and F1-scores while reducing development time by 90%. These results demonstrate AutoML's significant potential to enhance healthcare diagnostics by providing fast, accurate predictions without requiring specialized machine learning knowledge. The research confirms AutoGluon's effectiveness as a scalable solution for medical AI applications, with particular advantages in clinical decision support systems.