Deep Learning-Based Approach for Thyroid Dysfunction Prediction

Globally, thyroid dysfunction is a major health concern caused due to irregular hormone production by the thyroid gland. Millions of populations are getting affected by this disease regularly. Accurate diagnosis of thyroid dysfunction is crucial for effective treatment and management of the disease, but this is challenging given the condition’s complex and varied symptoms. In this paper, a deep learning- based neural network algorithm for generating predictions is constructed based on a dataset of approximately 3772 patient records with 28 features. The Artificial Neural Network (ANN) model was trained and evaluated using standard machine learning techniques and achieved high-level accuracy (98.8%) in identifying instances of thyroid dysfunction. The findings demonstrate that the proposed ANN model can be a reliable and effective tool for early diagnosis of thyroid dysfunction.