AI-Based Detection of Subclinical Hypothyroidism Using Clinical and Patient Data from Machine Learning Models

Subclinical hypothyroidism (SCH) is one of the most common but often unrecognised disorders of thyroid function, being defined by an elevated serum level of the thyroidstimulating hormone (TSH) and a normal level of free thyroxine (FT4) capable of causing metabolic imbalance, cardiovascular diseases, and even progression to overt hypothyroidism respectively. Step 1: Understand the NLP Problem Statement Traditional diagnostic methods depend on typical biochemical markers; however, they always fail to identify the slight changes in patient data that reflect the evolution of disease initiation. In this study, we present an AI-based detection model that leverages a series of machine learning algorithms to analyze the clinical and patient data to enhance the early detection of SCH. Using feature selection techniques and supervised learning models like Random Forest, Support Vector Machines, and Deep Neural Networks, it improves diagnostic accuracy by integrating electronic health records (EHRs), laboratory test results, and patient demographics. Predictive analytics assist with spotting at risk patients, enabling early identification and tailored treatment approaches. This study demonstrates the potential of AI in transforming endocrinology by providing scalable, automated, and low-cost solutions for the management of a common disease in this specialty, which can lead to timely diagnosis and preventive treatment. Ongoing work will center around larger datasets, increased model interpretability, and clinical integration of AIbased recommendations.