The　small-for-gestational-age　(SGA)　condition　often　causes　serious　problems.　Therefore,　identifying　the　risk　factors　for　SGA　is　important.　Traditional　statistical　methods　such　as　stepwise　logistic　regression　(LR)　have　been　widely　utilized　to　discover　possible　risk　factors.　However,　other　feature　selection　methods　from　machine　learning　field　have　rarely　been　employed　for　the　task.　In　this　paper,　a　comparison　of　five　feature　selection　methods　from　both　fields　for　SGA　risk　factors　analysis　is　conducted　for　the　first　time.　To　evaluate　their　performance,　four　classification　algorithms　are　used　to　construct　SGA　prediction　models.　The　evaluation　criteria　are　precision　and　the　area　under　the　receiver　operator　characteristic　curve.　Stepwise　LR　achieves　the　best　performance　among　the　five　feature　selection　methods,　because　it　conducts　both　a　univariate　significance　test　and　a　model　significance　test,　which　make　it　more　suitable　for　handling　the　complex　relations　among　features.　The　top　20　features　selected　by　each　feature　selection　method　and　the　27　features　selected　by　four　or　five　of　them　could　assist　physicians　to　revise　traditional　SGA　evaluation　models.　Ensemble　method　is　also　exploited　to　build　effective　SGA　prediction　models　based　on　the　feature　subsets,　which　is　indeed　superior　compared　with　the　individual　ones　shown　in　the　results.　©　2018　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature