An　accurate　and　efficient　Large-for-Gestational-Age　(LGA)　classification　system　is　developed　to　classify　a　fetus　as　LGA　or　non-LGA,　which　has　the　potential　to　assist　paediatricians　and　experts　in　establishing　a　state-of-the-art　LGA　prognosis　process.　The　performance　of　the　proposed　scheme　is　validated　by　using　LGA　dataset　collected　from　the　National　Pre-Pregnancy　and　Examination　Program　of　China　(2010-2013).　A　master　feature　vector　is　created　to　establish　primarily　data　pre-processing,　which　includes　a　features＇　discretization　process　and　the　entertainment　of　missing　values　and　data　imbalance　issues.　A　principal　feature　vector　is　formed　using　GridSearch-based　Recursive　Feature　Elimination　with　Cross-Validation　(RFECV)　+　Information　Gain　(IG)　feature　selection　scheme　followed　by　stacking　to　select,　rank,　and　extract　significant　features　from　the　LGA　dataset.　Based　on　the　proposed　scheme,　different　features　subset　are　identified　and　provided　to　four　different　machine　learning　(ML)　classifiers.　The　proposed　GridSearch-based　RFECV+IG　feature　selection　scheme　with　stacking　using　SVM　(linear　kernel)　best　suits　the　said　classification　process　followed　by　SVM　(RBF　kernel)　and　LR　classifiers.　The　Decision　Tree　(DT)　classifier　is　not　suggested　because　of　its　low　performance.　The　highest　prediction　precision,　recall,　accuracy,　Area　Under　the　Curve　(AUC),　specificity,　and　F1　scores　of　0.92,　0.87,　0.92,　0.95,　0.95,　and　0.89　are　achieved　with　SVM　(linear　kernel)　classifier　using　top　ten　principal　features　subset,　which　is,　in　fact　higher　than　the　baselines　methods.　Moreover,　almost　every　classification　scheme　best　performed　with　ten　principal　feature　subsets.　Therefore,　the　proposed　scheme　has　the　potential　to　establish　an　efficient　LGA　prognosis　process　using　gestational　parameters,　which　can　assist　paediatricians　and　experts　to　improve　the　health　of　a　newborn　using　computer　aided-diagnostic　system.