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Global performance of machine learning models to predict all-cause mortality: systematic review and meta-analysis
Felipe Mendes Delpino1, Ludmila Pereira Pimenta2, Diego Ferreira Gonzalez3
1Postgraduate Program in Nursing, Federal University of Pelotas, Gomes Carneiro, 01, Pelotas, Rio Grande do Sul, Brazil. fmdsocial@outlook.com.
Scientific Reports
|November 27, 2025
Summary
Machine learning models show high accuracy in predicting all-cause mortality. However, the models often exclude social variables, risking the perpetuation of health disparities and requiring local validation.
Area of Science:
- Medical Informatics
- Public Health
- Machine Learning
Background:
- Machine learning (ML) models are increasingly used for health predictions.
- Predicting all-cause mortality is crucial for public health interventions.
- A systematic review is needed to assess the performance and equity of ML models for mortality prediction.
Purpose of the Study:
- To systematically review and meta-analyze the performance of ML models in predicting all-cause mortality.
- To identify factors influencing model performance and assess equity considerations.
Main Methods:
- Systematic literature search in PubMed, LILACS, Web of Science, and Scopus.
- Meta-analysis of 88 studies using random-effects models.
- Heterogeneity assessed with I² statistics; quality evaluated using TRIPOD+AI.
Main Results:
- Pooled AUC of 0.831 (95% CI 0.797-0.865) with extreme heterogeneity (I²:100%).
- Most studies (89.8%) excluded social variables; equity-focused analyses and external validation were scarce (<10%).
- Model performance was similar across general/disease-specific populations and high-/low-income countries.
Conclusions:
- ML models demonstrate high predictive performance for all-cause mortality.
- Significant equity gaps exist due to the exclusion of social variables, risking perpetuation of disparities.
- Extreme heterogeneity necessitates local validation before public health implementation.
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