Diagnostic Performance of Machine Learning Algorithms for Predicting Heart Failure in Diabetic Patients: A Systematic Review and Meta‐Analysis

Background Heart failure is a significant complication in diabetic patients, and machine learning algorithms offer potential for early prediction. This systematic review and meta‐analysis evaluated the diagnostic performance of ML models in predicting HF among diabetic patients. Methods We searched PubMed, Web of Science, Embase, ProQuest, and Scopus, identifying 2830 articles. After deduplication and screening, 16 studies were included, with 7 providing data for meta‐analysis. Study quality was assessed using PROBAST+AI. A bivariate random‐effects model (Stata, midas, metadta) pooled sensitivity, specificity, likelihood ratios, and diagnostic odds ratio (DOR) for best‐performing algorithms, with subgroup analyses. Heterogeneity ( I 2 ) and publication bias were assessed. Results This meta‐analysis of seven studies evaluating machine learning models for heart failure detection demonstrated a pooled sensitivity of 84% (95% CI: 0.75–0.90), specificity of 86% (95% CI: 0.56–0.97), and an area under the ROC curve of 0.90 (95% CI: 0.87–0.93). The pooled positive likelihood ratio was 6.6 (95% CI: 1.2–35.9), and the negative likelihood ratio was 0.17 (95% CI: 0.08–0.36), with a diagnostic odds ratio of 39 (95% CI: 4–423). Significant heterogeneity was observed, primarily related to differences in study populations, machine learning algorithms, dataset sizes, and validation methods. No significant publication bias was detected. Conclusion Machine learning models demonstrate promising diagnostic accuracy for heart failure detection and have the potential to support early diagnosis and risk assessment in clinical practice. However, considerable heterogeneity across studies and limited external validation highlight the need for standardised development, prospective validation, and improved interpretability of ML models to ensure their effective integration into healthcare systems.