Abstract
BACKGROUND: Diabetic oral ulceration (DOU) is a prevalent and debilitating complication among diabetic patients, significantly impairing their quality of life and imposing substantial economic burdens. Studies indicate that over 90% of diabetic patients experience oral complications, with 45% suffering from oral ulcers. Clear diagnosis is crucial for effective clinical management and prognosis improvement. However, current diagnostic methods often fall short in early detection and intervention. Machine learning (ML) has shown promise in predicting disease development, yet no relevant predictive models for DOU have been established. METHODS: This study aimed to develop an ML-based predictive model for DOU using oral examination, clinical, and socioeconomic data. The dataset included 324 diabetic patients, with 127 DOU features. One-hundred-fold cross-validation was employed for model optimization and feature selection. Data preprocessing involved handling missing values, scaling different range values, and feature selection using techniques such as Variance Threshold (VT), Mutual Information (MI), and Variance Inflation Factor (VIF). Four prediction models, Support Vector Machine Classifier (SVC), Multi-layer Perceptron (MLP), Logistic Regression Classifier (LogReg), and Perceptron, were established and evaluated. RESULTS: The SVC model outperformed the other models, achieving an accuracy (ACC) of 0.95 and an area under the ROC curve (AUC) of 0.91. The top five features contributing to the model's predictions were the current number of oral ulcers, diminished oral functional capacity, number of decayed or missing teeth, possession of health insurance (commercial), and Low-Density Lipoprotein (LDL-C), accounting for 57.32% of the total importance. Oral examination indicators accounted for 46.46%, serum lipid markers for 6.93%, and sociodemographic factors, personal lifestyles, and cardiovascular diseases also played significant roles. CONCLUSION: The SVC model demonstrated superior performance and stability, making it suitable for predicting DOU occurrence and development in diabetic patients. This study's innovation lies in the comprehensive evaluation of multiple factors, including oral examinations, physiological indicators, self-management capabilities, and economic factors, to facilitate efficient DOU screening. The findings highlight the potential of ML in improving diagnostic accuracy and enabling timely interventions for DOU, ultimately contributing to better clinical management and patient outcomes. Future research should focus on validating the model across larger, multicenter cohorts and further exploring the long-term impact of ML-guided interventions on DOU management.