Why the study?
Predictive modeling for cardiovascular risk estimation is challenging, creating a need for soft computing and data mining tools that assist physicians in prompt clinical decisions.
Does an ensemble machine learning model improve predictive accuracy for diagnosing the recurrence of cardiovascular disease?
Does an ensemble machine learning model improve predictive accuracy for diagnosing the recurrence of cardiovascular disease?
An ensemble machine learning approach combining five classifiers provides high predictive accuracy for diagnosing cardiovascular disease recurrence.
No takes yet. Share an insight, caveat, or question.
May aid CV risk modeling in databases; leaves open prospective validation before clinical use.
Jan et al. (2018) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: