Unveiling Thyroid Disease Associations: An Exceptionality-Based Data Mining Technique

Abstract

Background: The prevalence of thyroid disease has seen a rapid increase in recent times, primarily attributed to the fast pace of lifestyles that often result in poor dietary choices, work-life imbalances, social stress, genetic mutations, and improved diagnostic capabilities. However, the precise contribution of these factors to thyroid disease remains a subject of controversy. Consequently, there is a pressing need to gain a comprehensive understanding of the related associations in order to potentially mitigate the associated morbidity and mortality rates. Methods: This study employed association rule mining techniques to reveal hidden correlations among complex and diverse epidemiological connections pertaining to thyroid disease associations. We proposed a framework which incorporates text mining and association rule mining algorithms with exceptionality measurement to simultaneously identify common and exception risk factors correlated with the disease through real-life digital health records. Two distinctive datasets were analyzed through two algorithms, and mutual factors were retained for interpretation. Results: The results confirmed that age, gender, and history of thyroid disease are risk factors positively related to subsequent thyroid cancer. Furthermore, it was observed that the absence of underlying chronic disease conditions, such as diabetes, hypertension, or obesity, are associated with reduced likelihood of being diagnosed with thyroid cancer. Conclusions: Collectively, the proposed framework demonstrates its sound feasibility and should be further recommended for different disease in-depth knowledge discovery.