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

Background: Oral mucosal lesions are widespread globally, have a high prevalence in clinical practice, and significantly impact patients' quality of life. However, their pathogenesis remains unclear. Recent evidences suggested that hematological parameters may play a role in their development. Our study investigated the differences in humoral immune indexes, serum vitamin B levels, and micronutrients among patients with oral mucosal lesions and healthy controls. Additionally, it evaluated a Random Forest machine learning model for classifying various oral mucosal diseases based on peripheral blood biomarkers.

Methods: We recruited 237 patients with recurrent aphthous ulcers (RAU), 35 with oral lichen planus (OLP), 67 with atrophic glossitis (AG), 35 with burning mouth syndrome (BMS), and 82 healthy controls. Clinical data were analyzed by SPSS 24 software. Serum levels of immunoglobulins (IgG, IgA, IgM), complements (C3, C4), vitamin B (VB1, VB2, VB3, VB5), serum zinc (Serum Zn), serum iron (Serum Fe), unsaturated iron-binding capacity (UIBC), total iron-binding capacity (TIBC), and iron saturation (Iron Sat) were measured and compared among groups. A Random Forest model was applied to analyze a dataset comprising 319 samples with eight key biomarkers.

Results: Significant differences were observed between the oral mucosal diseases groups and controls in the serum levels of VB2, VB3, VB5, zinc, iron, TIBC, and Iron Sat. Specifically, serum levels of VB2 and VB3 were significantly higher in patients compared to controls (*p < 0.05), while levels of VB5, Serum Zn, Serum Fe, TIBC, and Iron Sat were significantly lower (*p < 0.05). No significant differences were found for C3, C4, IgG, IgM, IgA, VB1, and UIBC. The optimized Random Forest model demonstrated high performance, and effectively classified different disease groups, though some overlap between groups was noted. Feature importance analysis, based on the Mean Decrease Accuracy and Gini Index, identified VB2, VB3, Serum Fe, TIBC, and Serum Zn as key biomarkers, indicating their potential in distinguishing oral mucosal diseases.

Conclusion: 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.