Diagnosing　infants　who　are　small　for　gestational　age　(SGA)　at　early　stages　could　help　physicians　to　introduce　interventions　for　SGA　infants　earlier.　Machine　learning　(ML)　is　envisioned　as　a　tool　to　identify　SGA　infants.　However,　ML　has　not　been　widely　studied　in　this　field.　To　develop　effective　SGA　prediction　models,　we　conducted　four　groups　of　experiments　that　considered　basic　ML　methods,　imbalanced　data,　feature　selection　and　the　time　characteristics　of　variables,　respectively.　Infants　with　SGA　data　collected　from　2010　to　2013　with　gestational　weeks　between　24　and　42　were　detected.　Support　vector　machine　(SVM),　random　forest　(RF),　logistic　regression　(LR)　and　Sparse　LR　models　were　trained　on　10-fold　cross　validation.　Precision　and　the　area　under　the　curve　(AUC)　of　the　receiver　operator　characteristic　curve　were　evaluated.　For　each　group,　the　performance　of　SVM　and　Sparse　LR　was　similarly　well.　LR　without　any　sparsity　penalties　performed　worst,　possibly　caused　by　the　overfitting　problem.　With　the　combination　of　handling　imbalanced　data　and　feature　selection,　the　RF　ensemble　classifier　performed　best,　which　even　obtained　the　highest　AUC　value　(0.8547)　with　the　help　of　expert　knowledge.　In　other　cases,　RF　performed　worse　than　Sparse　LR　and　SVM,　possibly　because　of　fully　grown　trees.