en statistics statistics Original Article Original Article <span style="font-size:14px;"><span style="font-family:Times New Roman;"><span style="line-height:14pt"><b><i><span style="color:blue">Background</span></i></b><i><span style="color:blue">:</span></i> The aim of this study was developing and comparative analyzing prediction models using a Cox proportional hazards model with and without frailty, random survival forests (RSF) and survival support vector regression (SVR).</span><br> <span style="line-height:14pt"><b><i><span style="color:blue">Methods:</span></i></b> In this study, 2800 patients with STEMI have been used and two machine learning methods for survival analysis have been applied: RSF and SVR, then the Cox model with and without frailty has been employed. The main outcome was 1-year mortality after STEMI. In this study, 16 variables have missing data. After applying four multiple imputation via chained equations methods, the &ldquo;Sample&rdquo; algorithm was selected as the appropriate model with complete data and the modeling process was continued with this data and Hazard Ratio (HR) were calculated.</span><br> <span style="line-height:14pt"><b><i><span style="color:blue">Results:</span></i></b> Overall, 1628 (58.1%) patients received primary percutaneous coronary intervention and 737 (26.3%) received thrombolytic therapy. Based on the experimental results, between all the models, the Cox with frailty model performed the best, with the highest overall C-index (0.891) and time-dependent area under the curve (0.9134) and the least Brier score (0.0458). Ever smoking (HR= 1.46), systolic blood pressure (HR= 0.98), left ventricular ejection fraction (HR= 0.96), glomerular filtration rate (HR= 0.96), and reperfusion therapy (No reperfusion HR= 2.71) independently associated with 1-year mortality of STEMI patients.</span><br> <span style="line-height:14pt"><b><i><span style="color:blue">Conclusion</span></i></b><i><span style="color:blue">:</span></i> The findings suggest that there are advantages in developing frailty models further than the fundamental Cox proportional hazards regression for estimating the likelihood of survival for STEMI patients to account for the unobserved heterogeneity in grouped observations. </span></span></span><br> &nbsp; Survival analysis, Machine-learning, Myocardial Infarction, Frailty model. 775 790 http://caspjim.com/browse.php?a_code=A-10-3618-1&slc_lang=en&sid=1 Mansour Rezaei Mrezaei@kums.ac.ir 100319475328460054681 100319475328460054681 No Social Development and health promotion research center, Health Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran. Maryam Montaseri Maryam.Montaseri@yahoo.com 100319475328460054682 100319475328460054682 Yes School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran. Shayan Mostafaei shayan.mostafaei@ki.se 100319475328460054683 100319475328460054683 No Department of Medical Epidemiology and Biostatistics Armin Khayati armin.khayati@hafez.shirazu.ac.ir 100319475328460054684 100319475328460054684 No Department of Computer Science and Engineering, School of Electrical and Computer Engineering Mohammad Taheri motaheri@shirazu.ac.ir 100319475328460054685 100319475328460054685 No Department of Computer Science and Engineering, School of Electrical and Computer Engineering,