Machine learning for the prediction of diabetes-related amputation: a systematic review and meta-analysis of diagnostic test accuracy.

Abstract

Although machine learning is frequently used in medicine for predictive purposes, its accuracy in diabetes-related amputation (DRA) remains unclear. From establishing the database until December 2024, we conducted a comprehensive search of PubMed, Web of Science (WoS), Embase, Scopus, Cochrane Library, Wanfang, and the China National Knowledge Index (CNKI). The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), area under the curve (AUC), and Fagan plot analysis were used to assess the overall test performance of machine learning. Moreover, subgroup analysis and meta-regression were performed to search for possible sources of heterogeneity. Finally, sensitivity analysis and Deeks' funnel plot asymmetry test were used to evaluate the stability and publication bias, respectively. In the end, seven publications were included in this meta-analysis. The overall pooled diagnostic data were as follows: sensitivity, 0.72 (95% CI 0.69-0.75); specificity, 0.89 (95% CI 0.84-0.93); PLR, 3.62 (95% CI 3.36-3.89); NLR, 0.32 (95% CI 0.30-0.35); DOR, 13.55 (95% CI 11.72-15.67). The AUC was 0.81 (95% CI 0.77-0.84). The Fagan plot analysis showed that the positive post-test probability is 62% and the negative post-test probability is 7%. Subgroup analysis and meta-regression showed that both the level of bias and the year of publication were sources of heterogeneity in sensitivity and specificity. Sensitivity analysis confirmed the robustness of the results after excluding three outlier studies. The Deeks' funnel plot suggests that publication bias has no statistical significance (P > 0.05). In summary, our results suggest the moderate accuracy of machine learning in predicting DRA.