Machine Learning Models for Predicting the Risk of Hard-to-Heal Diabetic Foot Ulcers in a Chinese Population

Abstract

Background Early detection of hard-to-heal diabetic foot ulcers (DFUs) is vital to prevent a poor prognosis. The purpose of this work was to employ clinical characteristics to create an optimal predictive model of hard-to-heal DFUs (failing to decrease by >50% at 4 weeks) based on machine learning algorithms. Methods A total of 362 DFU patients hospitalized in two tertiary hospitals in eastern China were enrolled in this study. The training dataset and validation dataset were split at a ratio of 7:3. Univariate logistic analysis and clinical experience were utilized to screen clinical characteristics as predictive features. The following six machine learning algorithms were used to build prediction models for differentiating hard-to-heal DFUs: support vector machine, the naïve Bayesian (NB) model, k-nearest neighbor, general linear regression, adaptive boosting, and random forest. Five cross-validations were employed to realize the model’s parameters. Accuracy, precision, recall, F1-scores, and AUCs were utilized to compare and evaluate the models’ efficacy. On the basis of the best model identified, the significance of each characteristic was evaluated, and then an online calculator was developed. Results Independent predictors for model establishment included sex, insulin use, random blood glucose, wound area, diabetic retinopathy, peripheral arterial disease, smoking history, serum albumin, serum creatinine, and C-reactive protein. After evaluation, the NB model was identified as the most generalizable model, with an AUC of 0.864, a recall of 0.907, and an F1-score of 0.744. Random blood glucose, C-reactive protein, and wound area were determined to be the three most important influencing factors. A corresponding online calculator was created ( https://predicthardtoheal.azurewebsites.net/ ). Conclusion Based on clinical characteristics, machine learning algorithms can achieve acceptable predictions of hard-to-heal DFUs, with the NB model performing the best. Our online calculator can assist doctors in identifying the possibility of hard-to-heal DFUs at the time of admission to reduce the likelihood of a dismal prognosis.

Protocol for Metadata and Image Collection at Diabetic Foot Ulcer Clinics: Enabling Research in Wound Analytics and Deep Learning

“…To accurately classify DFUs, DL uses deep neural networks, most commonly convolutional neural networks (CNN) that can e ciently extract informative features for image classi cation [29]. To optimize the e ciency of DL and ML technologies, collecting demographic data such as age, sex, illness and DFU history, prior alcohol and smoking usage, wound characteristics, as well as comorbidities through case report forms (CRF), can aid in diagnosing underlying infection and lead to AI algorithms that successfully predict hard-to-heal DFUs [30,31]. Other computerized solutions for DFU diagnosis, such as depth cameras, RGB sensors, and thermometry, are advanced imaging tools and have proven effective in medical settings [26,32,33].…”