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.

Keywords:

• hard-to-heal
• diabetic foot ulcers
• machine learning
• classification

Abbreviations

AdaBoost, adaptive boosting; AUC, area under the curve; DFUs, diabetic foot ulcers; GLM, general linear regression; KNN, k-nearest neighbor; NB, naïve Bayesian; PAR, percent area reduction; RF, random forest; ROC, Receiver operating characteristic; SVM, support vector machine; USWR, the US wound registry; UT, University of Texas.

Data Sharing Statement

The authors declare that the main data supporting the findings of this study are available within the article. Extra data are available from the corresponding author upon request.

Ethics Approval and Consent to Participate

Our study protocol had been approved by the Ethics Committee of Nanjing Drum Tower Hospital (No. 2020-10901). Informed consent of the participants was waived because of the retrospective study design and the use of anonymized clinical data.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 81974288).