Thyroid Insight: navigating disease data through interactive visualization with prediction

The thyroid gland, located in the neck, plays a crucial role in regulating metabolism, growth, and energy through hormones such as thyroxine (T4) and triiodothyronine (T3). Disorders such as hypothyroidism, hyperthyroidism, and thyroid cancers are often linked to iodine deficiency and genetics. However, limited public awareness and delayed diagnosis can lead to severe health complications. Analysing thyroid disease data is challenging due to its complexity and unstructured nature, making advanced analytical techniques essential. This paper addresses these challenges by developing an interactive dashboard with predictive capabilities. The system integrates Big Data analytics and predictive modelling to improve understanding and support proactive management of thyroid health. It follows a structured methodology, including planning, analysis, design, development, and testing, using data from Kaggle and the UCI Machine Learning Repository. The dashboard employs Microsoft Power BI for visualizations and the Random Forest algorithm for predictive modelling. Evaluation using the Technology Acceptance Model (TAM) with 35 respondents produced encouraging results across dimensions such as Perceived Ease of Use (4.28), Perceived Usefulness (4.61), Attitude (4.54), and Intention to Use (4.50). User feedback highlighted the dashboard's intuitive design, clarity in presenting complex information, and potential to raise awareness about thyroid health. While the findings are based on a limited evaluation, results indicate that the system may contribute to improving public awareness, supporting early detection, and empowering users to make informed health decisions. With future improvements, such as real-time data integration and expanded datasets, the system could further enhance healthcare practices and public education regarding thyroid diseases, promoting proactive health management.