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Purpose: Plenty of studies have explored the diagnosis and prognosis of IgA nephropathy (IgAN) based on machine learning (ML), but the accuracy lacks the support of evidence-based medical evidence. We aim at this problem to guide the precision treatment of IgAN. Methods: Embase, Pubmed, Cochrane Library, and Web of Science were searched systematically until February 24th, 2024, for publications on ML-based diagnosis and prognosis of IgAN. Subgroup analysis or meta-regression was conducted according to modeling method, follow-up time, endpoint definition, and variable type. Further, the rank sum test was applied to compare the discrimination ability of prognosis. Results: A total of 47 studies involving 51,935 patients were eligible. Among the 38 diagnostic models, the pooled C-index was 0.902 (95 % CI: 0.878–0.926) in 27 diagnostic models. Of the 162 prognostic models, the C-index for model discrimination of 144 prognostic models was 0.838 (95 % CI: 0.827–0.850) in training. The overall discrimination ability of prognosis was as follows: COX regression > new ML models (e.g. ANN, DT, RF, SVM, XGBoost) > traditional ML models (logistic regression) > Naïve Bayesian network (P |
