Intelligent Symptom Analysis and Report Interpretation for Thyroid Disorder Prediction Using Machine Learning

The thyroid gland, situated at the front of the neck, is essential for producing hormones that regulate many of the body’s critical functions. This paper presents a machine learning-based system aimed at enhancing early detection and providing personalized care for thyroid disorders. Leveraging the Random Forest algorithm chosen for its robust classification abilities the system incorporates two key models: an initial assessment tool that examines self-reported symptoms to determine if further diagnostic testing is recommended, and a diagnostic model that interprets test results to identify specific thyroid disorders. Additionally, the system provides customized diet and exercise suggestions. With an accuracy of 85%, the system effectively supports early symptom analysis and accurate diagnosis. By combining symptom evaluation with tailored health guidance, this approach enables timely detection, accurate diagnostics, and effective management of thyroid conditions, offering personalized insights for patients and support for healthcare providers.