Application of machine learning for the analysis of peripheral blood biomarkers in oral mucosal diseases: a cross-sectional study

Our study identified significant associations between the contents of VB2, VB3, VB5, Serum Fe, Serum Zn, and other micronutrients and oral mucosal lesions. It suggested that regulating these micronutrient levels could be essential for preventing and curing such lesions. The Random Forest model demonstrated high accuracy (94.68%) in classifying disease groups, emphasizing the potential of machine learning to enhance diagnostic precision in oral mucosal diseases. Future research should focus on validating these findings in larger cohorts and exploring alternative machine-learning algorithms to improve diagnostic accuracy further.