A Deep Learning Approach to Early Detection and Prediction of Thyroid Disease

This research develops a deep learning-based system for the early diagnosis and treatment of thyroid diseases, addressing the need for accurate and timely diagnostic solutions due to the high prevalence of conditions like hypothyroidism and hyperthyroidism. The study aims to facilitate early detection using a sequential deep learning model trained on diverse patient data. Performance is evaluated using accuracy, precision, recall, and F1-score, with comparisons to conventional methods. The model achieved 98.86% accuracy on 1,402 unseen test samples, with 100% precision for the negative class and 92.4% for the positive class. Its high recall rates (99.8% for negative and 99.5% for positive) demonstrate excellent sensitivity and generalization, contributing to advancements in machine learning-based medical diagnostics.