Why the study?
Accurate risk stratification after MI is essential, but whether machine learning offers an advantage over conventional statistical models in real-world clinical settings remains uncertain.
Do machine learning models improve the prediction of 3-year all-cause mortality compared to Cox regression in patients after myocardial infarction?
Population
965 patients enrolled in a structured post-MI care program between 2018 and 2022
Comparison
Cox proportional hazards regression vs three machine learning models
Design
Retrospective cohort study with temporal validation
Follow-up
3-year
Key result
Machine learning models did not outperform Cox regression in predicting 3-year all-cause mortality after myocardial infarction (AUC 0.712-0.738 vs 0.742; P=0.59).
Authors
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ML models offer no advantage over Cox regression for post-MI mortality prediction in moderate cohorts; leaves open utility with larger datasets or richer predictors.
Cohort (n=965)
Do machine learning models improve the prediction of 3-year all-cause mortality compared to Cox regression in patients after myocardial infarction?
p-value: p=0.59
Machine learning models did not outperform traditional Cox proportional hazards regression for predicting 3-year mortality in a moderate-sized post-MI cohort.
Szymańska-Łyczkowska et al. (2026) conducted a cohort in myocardial infarction (n=965). Machine learning models vs. Cox proportional hazards regression was evaluated on 3-year all-cause mortality (p=0.59). Machine learning models did not outperform Cox regression in predicting 3-year all-cause mortality after myocardial infarction (AUC 0.712-0.738 vs 0.742; P=0.59).