A　newborn　with　a　birth　weight　above　the　90th　percentile　of　same　gestational　age　is　termed　as　large　for　gestational　age.　Large　for　gestational　age　suffers　from　serious　complications　during　and　after　the　antepartum　period　because　they　do　not　get　earlier　identification　of　the　disease.　Earlier　recognition　of　large　for　gestational　age　infants　could　slow　progression　and　prevent　further　complication　of　the　disease.　In　medical　science,　prevention　and　mitigation　of　disease　require　examination　of　biochemical　indicators.　Machine　learning　has　been　evolved　and　envisioned　as　a　tool　to　predict　large　for　gestational　age　infants　with　most　deterministic　characteristics.　This　study　aims　to　identify　most　deterministic　biochemical　indicators　for　large　for　gestational　age　prediction　with　minimal　computational　overhead.　To　the　best　of　my　knowledge,　this　is　the　first　time　a　study　is　carried　out　to　identify　the　most　deterministic　risk　factors　associated　with　large　for　gestational　age　and　to　develop　large　for　gestational　age　prediction　model　using　machine　learning　techniques.　To　develop　an　efficient　large　for　gestational　age　prediction　model,　we　conducted　three　group　of　experiments　that　considered　basic　machine　learning　methods;　feature　selection;　and　imbalanced　data,　respectively.　Support　vector　machine,　logistic　regression,　Naive　Bayes　and　Random　Forest　were　trained　using　tenfold　cross-validation　on　large　for　gestational　age　dataset;　we　selected　precision　and　area　under　the　curve　as　a　performance　evaluation　metrics;　information　gain　an　entropy-based　feature　selection　method　was　adopted　to　rank　features;　we　introduced　an　ensemble　data　imbalance　technique　in　the　last　group　of　experiments.　For　each　group　of　experiments,　support　vector　machine　performed　best　compared　to　other　machine　learning　classifiers　by　producing　the　highest　prediction　precision　score　of　85%.　All　of　the　classifiers　performed　best　with　thirty　ranked　features　subset,　which　validates　the　applied　method　to　recognize　the　most　deterministic　risk　factors　associated　with　large　for　gestational　age　prediction.