Journal of Artificial Intelligence, Machine Learning and Soft Computing

Authors:  Diksha Tardekar, Swayam Suryavanshi, Vaishnavi Chavan, Sagar Chavan

Abstract: Thyroid diseases are affecting millions of people globally, and timely detection is crucial for effective treatment. However, traditional methods such as blood tests and ultrasounds have limitations in terms of accuracy, cost, and availability. In recent years, machine learning techniques have shown promise in thyroid detection by leveraging large datasets and advancements in computational power. This paper reviews state-of-the-art machine learning techniques for thyroid detection, including classification, clustering, and deep learning methods. We discuss the challenges and limitations of existing approaches, such as data imbalance, interpretability, and model generalization. Additionally, we highlight potential future research directions, including the integration of multi-modal data, explainable AI, and personalized medicine.

Keywords: Machine Learning, Thyroid Detection, Thyroid Detection Using Machine Learning