PREDICTION OF FOOT ULCER RECURRENCE IN ELDERLY DIABETIC PATIENTS BASED ON THE MAMBA MODEL

Diabetic foot ulcers (DFUs), a common complication in diabetic patients, are characterized by a high recurrence rate and a significant impact on patients’ quality of life. Traditional prediction methods often struggle with limited accuracy. In this study, we propose a novel recurrence prediction approach based on the Mamba model to enhance predictive performance. Using clinical time-series data from 138 elderly diabetic patients, we evaluated the Mamba-based method against traditional machine learning models, including Random Forest, Decision Tree, K-Nearest Neighbors (KNNs), and XGBoost. Experimental results demonstrate that the Mamba model outperforms baseline models in terms of accuracy, precision, and recall. These findings highlight the effectiveness of the proposed method in DFU recurrence prediction and its potential to support clinical decision-making and improve patient outcomes.