Abstract
It is to develop a predictive model utilizing machine learning techniques to promptly identify patients with diabetic foot ulcers (DFU) who may require major amputation upon their initial admission. A total of 598 DFU patients were admitted to a tertiary hospital in Beijing. We employed synthetic minority oversampling technique to address the class imbalance of the target variable in the original dataset. A Lasso regularization analysis identified 17 feature variables for inclusion in the model: age, diabetes duration, wound size, history of peripheral neuropathy, history of atrial fibrillation, white blood cell count, C-reactive protein (CRP), procalcitonin, glycated hemoglobin (HbA1c), myoglobin (Mb), troponin (Tn), blood urea nitrogen, serum albumin, triglycerides (TG), low-density lipoprotein cholesterol, multidrug-resistant infection, vascular intervention. Subsequently, risk prediction models were independently developed by using these feature variables based on six machine learning algorithms: logistic regression, random forest, support vector machine, K-nearest neighbors, gradient boosting machine (GBM), and extreme gradient boosting (XGBoost). The performance of six models was evaluated to select the best model for predicting the risk of major amputation. GBM was identified as the best predictive model (accuracy 0.9408, precision 0.9855, recall 0.8553, F1-score 0.9158, and AUC 0.9499). This model also highlights the importance ranking of feature variables associated with predicting the risk of major amputation, with the top five variables being the presence of multidrug-resistant infection, CRP, diabetes duration, Tn, age. It is an effective machine learning method that GBM model is used to predict the risk of major amputations in diabetic foot patients.