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 u

SUBMITTER: Chen Z 

PROVIDER: S-EPMC12065772 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature