This　work　studies　the　problem　of　identifying　risk　factors　of　Small　for　Gestational　Age　(SGA)　and　building　classifiers　for　SGA　prediction.　Recently,　SGA　infants　have　received　more　and　more　concerns　as　this　illness　brings　many　difficulties　to　them　along　with　their　whole　life.　Some　experts　have　begun　to　study　the　risk　factors　of　SGA　onset　by　using　traditional　statistical　ways.　Others　have　used　logistic　regression　(LR)　to　construct　SGA　prediction　models.　Meanwhile,　machine　learning　have　evolved　and　envisioned　as　a　tool　able　to　potentially　identify　babies　with　SGA.　This　work　tests　several　feature　selection　methods.　Based　on　the　risk　factors　obtained　through　them,　it　trains　support　vector　machine,　random　forest,　and　LR　models　and　evaluates　them　via　10-fold　cross　validation　in　terms　of　precision　and　area　under　the　curve　of　receiver　operator　characteristic　curve.　The　results　show　that　sparse　LR　of　the　wrapper　algorithms　owns　the　best　feature　selection　effectiveness.　In　addition,　this　work　compares　data　driven　factors　and　knowledge　driven　factors　and　shows　that　the　feature　selection　is　necessary　and　effective.　Among　the　trained　classifiers,　the　LR　model　achieves　the　best　performance　on　the　data　driven　factors.