BACKGROUND: Autoimmune thyroiditis (AIT), a common autoimmune disease, is a complex disease with an increasing incidence and an unknown pathogenesis that awaits the refinement of diagnostic methods and identification of diagnostic biomarkers to improve screening to identify patients at high risk of AIT earlier and provide the potential effective therapeutic drugs. PATIENTS AND METHODS: All samples for this study were from a cross-sectional survey, which was conducted among adults in two regions of Anhui Province, China. Ten representative samples (n RESULTS: The diagnostic model included three diagnostic genes (FGFR2, CCR1, IL1B). All ROC curves (AUC >
0.7) results suggested that the diagnostic model and the diagnostic genes had reliable predictive power. The results of logistic regression analysis showed that the three diagnostic genes were significant for AIT. The results of GSVA and immunoinfiltration analysis demonstrated that the diagnostic genes have significant negative or positive regulatory effect in immune mechanisms of AIT and the diagnostic model implements immune-related prediction algorithms. Finally, the small molecular compounds (Acetaminophen and Albuterol) were screened as the potential therapeutic drugs for AIT. CONCLUSION: Using machine learning and bioinformatics techniques, this study developed and validated an AIT diagnostic model, explored the diagnostic model's prediction mechanism, verified three potential diagnostic biomarkers by experiments and predicted two small molecule therapeutic drugs.