Published Paper Details:

Thyroid issues, including hyperthyroidism and hypothyroidism, affect millions of people around the globe and are notoriously challenging to diagnose accurately due to the complex interplay of clinical, biochemical, and imaging data. In this paper, we introduce a novel Dynamic Selection Hybrid Model (DSHM) that leverages advanced machine learning techniques to enhance the accuracy, efficiency, and personalization of thyroid treatment. To optimize predictive performance and adapt to evolving clinical data, the proposed model combines the strengths of various machine learning algorithms, such as decision trees, support vector machines (SVM), and deep learning networks, within a dynamic feature selection framework. Our hybrid approach features a two-stage process for selecting relevant characteristics: dynamic filtering and ensemble-based selection. These methods adapt to the available data, ensuring that the most pertinent features are utilized for accurate thyroid diagnosis and treatment recommendations. The model takes into account individual differences in thyroid conditions by responding to emerging data patterns and integrating clinical, laboratory, and demographic information for a more thorough and personalized evaluation. Additionally, the use of ensemble learning enhances robustness and minimizes the risk of overfitting, leading to improved generalization and predictive accuracy. This research not only enhances the role of machine learning in healthcare but also marks a significant advancement in the use of intelligent systems for personalized thyroid treatment, providing a more flexible and dependable solution.