en آمار زیستی Bio-statistics تحقیقی Original article <div style="text-align: justify;"><strong><em>Background and objectives:</em></strong> Application of statistical machine learning methods such as ensemble based approaches in survival analysis has been received considerable interest over the past decades in time-to-event data sets. One of these practical methods is survival forests which have been developed in a variety of contexts due to their high precision, non-parametric and non-linear nature. This article aims to evaluate the performance of survival forests by comparing them with Cox-proportional hazards (CPH) model in studying first birth interval (FBI).<br> <strong><em>Methods</em></strong>: A cross sectional study in 2017 was conducted by the stratified random sampling and a structured questionnaire to gather the information of 610, 15-49-year-old married women in Tehran. Considering some influential covariates on FBI, random survival forest (RSF) and conditional inference forest (CIF) were constructed by bootstrap sampling method (1000 trees) using R-language packages. Then, the best model is used to identify important predictors of FBI by variable importance (VIMP) and minimal depth measures.<br> <strong><em>Results:</em></strong> According to prediction accuracy results by out-of-bag (OOB) C-index and integrated Brier score (IBS), RSF outperforms CPH and CIF in analyzing FBI (C-index of 0.754 for RSF vs 0.688 for CIF and 0.524 for CPH and IBS of 0.076 for RSF vs 0.086 for CIF and 0.107 for CPH). Woman&rsquo;s age was the most important predictor on FBI.<br> <strong><em>Conclusions: </em></strong>Applying suitable method in analyzing FBI assures the results which be used for making policies to overcome decrement in total fertility rate.</div> Survival Analysis,Machine Learning,Cox-proportional hazards model,First Birth Intervals. 11 23 http://goums.ac.ir/jorjanijournal/browse.php?a_code=A-10-345-2&slc_lang=en&sid=1 Mahsa Saadati mahsa.saadati@gmail.com 10031947532846005388 10031947532846005388 No Associate professor of National Population Studies & Comprehensive Management Institute, Tehran, Iran. Arezoo Bagheri abagheri_000@yahoo.com 10031947532846005389 10031947532846005389 Yes Associate professor of National Population Studies & Comprehensive Management Institute