Comparison of the performance of random forest and decision tree models in predicting the risk of poor prognosis in diabetic foot patients

Objective To analyze the performance of random forest and decision tree models in predicting the risk of poor prognosis in diabetic foot patients, and to select the optimal risk prediction model for prognosis assessment in diabetic foot patients. Methods The medical records of 70 patients with diabetic foot admitted to Anqing First People's Hospital of Anhui Medical University from January 2021 to January 2024 were retrospectively analyzed, and the patients were divided into good prognosis group (n =48) and poor prognosis group (n =22) according to their prognosis. The data of the two groups were compared to screen the factors influencing the prognosis of diabetic foot patients. According to the ratio of 7∶3, the patients were divided into the training set and the test set, which were used to construct the random forest model and the decision tree model respectively, and the prediction performance of the models was verified. Results The duration of diabetes≥10 years, Wagner grade 3 to 4, C-reactive protein (CRP)≥20 mg/L, procalcitonin (PCT)≥0.5 ng/mL, and interleukin-6 (IL-6)≥30 mg/L in the poor prognosis group were significantly higher than those in the good prognosis group (P＜0.05). The area under the ROC curve (AUC) of random forest model and decision tree model to predict poor prognosis of diabetic foot patients in the test set were 0.918 (95%CI: 0.880-0.962) and 0.801 (95%CI: 0.716-0.852), the sensitivity was 94.78%, 82.65%, the specificity was 80.11%, 75.14%, the accuracy rate was 96.06%, 80.02%, the recall rate was 93.11%, 73.37%, the precision rate was 94.12%, 85.54%, respectively. Delong test results showed that the AUC of random forest model for predicting poor prognosis of diabetic foot patients was significantly greater than that of decision tree model in the test set (D=-3.648, P=0.012). Conclusion Both random forest model and decision tree model have good predictive performance in predicting the risk of poor prognosis in diabetic foot patients, and random forest model is significantly better than decision tree model in predicting poor prognosis in diabetic foot patients.