Machine learning prediction of diabetic foot ulcers in the inpatient population

Vascular. 2022 Dec;30(6):1115-1123. doi: 10.1177/17085381211040984. Epub 2021 Aug 30.


Background: The objective of this study was to create an algorithm that could predict diabetic foot ulcer (DFU) incidence in the in-patient population.

Materials and methods: The Nationwide Inpatient Sample datasets were examined from 2008 to 2014. The International Classification of Diseases 9th Edition Clinical Modification (ICD-9-CM) and the Agency for Healthcare Research and Quality comorbidity codes were used to assist in the data collection. Chi-square testing was conducted, using variables that positively correlated with DFUs. For descriptive statistics, the Student T-test, Wilcoxon rank sum test, and chi-square test were used. There were six predictive variables that were identified. A decision tree model CTREE was utilized to help develop an algorithm.

Results: 326,853 patients were noted to have DFU. The major variables that contributed to this diagnosis (both with p < 0.001) were cellulitis (OR 63.87, 95% CI [63.87-64.49]) and Charcot joint (OR 25.64, 95% CI [25.09-26.20]). The model performance of the six-variable testing data was 79.5% (80.6% sensitivity and 78.3% specificity). The area under the curve (AUC) for the 6-variable model was 0.88.

Conclusion: We developed an algorithm with a 79.8% accuracy that could predict the likelihood of developing a DFU.

Keywords: Diabetic foot ulcer; artificial intelligence; machine learning; wound care.

MeSH terms

  • Comorbidity
  • Diabetes Mellitus* / epidemiology
  • Diabetic Foot* / diagnosis
  • Diabetic Foot* / epidemiology
  • Humans
  • Incidence
  • Inpatients
  • Machine Learning