Predicting In‐Hospital Mortality in Patients With Acute Myocardial Infarction: A Comparison of Machine Learning Approaches
ML algorithms, particularly RF, effectively predict in-hospital mortality in AMI patients, highlighting critical predictors such as LVEF and biochemical markers. These insights offer valuable tools for enhancing clinical decision-making and improving patient outcomes.
Authors
- Babak Sattartabar (ORCID: https://orcid.org/0000-0003-4510-325X)
- Yaser Jenab (ORCID: https://orcid.org/0000-0002-8291-8637)
- Kaveh Hosseini (ORCID: https://orcid.org/0000-0001-5676-3099)
- Hamidreza Soleimani (ORCID: https://orcid.org/0000-0003-2802-4613)
- Soroush Najdaghi (ORCID: https://orcid.org/0009-0001-4555-5718)
- Farzad Masoudkabir (ORCID: https://orcid.org/0000-0001-8098-7121)
- Haleh Ashraf (ORCID: https://orcid.org/0000-0001-6687-995X)
- Mehdi Mehrani (ORCID: https://orcid.org/0000-0001-9831-1074)
- Parham Samimisedeh (ORCID: https://orcid.org/0000-0002-3667-8142)
- Delaram Narimani Davani (ORCID: https://orcid.org/0009-0007-3881-0040)
- Parham Dastjerdi (ORCID: https://orcid.org/0009-0007-3386-7697)
- Hedieh Shayesteh
Institutions
- Isfahan University of Medical Sciences (IR)
- Jahrom University of Medical Sciences (IR)
- Tehran University of Medical Sciences (IR)
Publication Details
- Journal
- Clinical Cardiology
- Published
- 2025-03-27
- DOI
- https://doi.org/10.1002/clc.70124
- Citations
- 2
- Primary Topic
- Acute Myocardial Infarction Research
- Type
- article
- Field-Weighted Citation Impact
- 2.78