Enhancing diabetic foot ulcer prediction with machine learning: A focus on Localized examinations

作者：Wang Xiaoling, Zhu Shengmei, Wang BingQian, Li Wen, Shuyan Gu, Hanbei Chen, Qin Chenjie, Yao Dai, Jutang Li · 发表于：Heliyon · 年份：2024 · DOI：10.1016/j.heliyon.2024.e37635 · 被引用次数：16 · 研究领域：Diabetic Foot Ulcer Assessment and Management、Peripheral Artery Disease Management、Lower Extremity Biomechanics and Pathologies

Background: diabetices foot ulcer (DFU) are serious complications. It is crucial to detect and diagnose DFU early in order to provide timely treatment, improve patient quality of life, and avoid the social and economic consequences. Machine learning techniques can help identify risk factors associated with DFU development. Objective: The aim of this study was to establish correlations between clinical and biochemical risk factors of DFU through local foot examinations based on the construction of predictive models using automated machine learning techniques. Methods: The input dataset consisted of 566 diabetes cases and 50 DFU risk factors, including 9 local foot examinations. 340 patients with Class 0 labeling (low-risk DFU), 226 patients with Class 1 labeling (high-risk DFU). To divide the training group (consisting of 453 cases) and the validation group (consisting of 113 cases), as well as preprocess the data and develop a prediction model, a Monte Carlo cross-validation approach was employed. Furthermore, potential high-risk factors were analyzed using various algorithms, including Bayesian BYS, Multi-Gaussian Weighted Classifier (MGWC), Support Vector Machine (SVM), and Random Forest Classifier (RF). A three-layer machine learning training was constructed, and model performance was estimated using a Confusion Matrix. The top 30 ranking feature variables were ultimately determined. To reinforce the robustness and generalizability of the predictive model, an independent d...