Gestational　weight　is　an　essential　parameter　for　the　pediatrician　to　clinically　evaluate　the　health　of　both　neonate　and　the　mother.　During　the　last　several　decades,　an　increase　in　the　prevalence　of　Large　for　Gestational　Age　(LGA)　neonate　is　reported　and　several　researchers　engaged　themselves　to　discover　the　cause.　Most　of　them　conducted　observational　or　retrospective　studies　that　used　simple　statistical　test　(i.e.　univariate/multivariate　logistic　regression　etc.,).　However,　machine　learning　schemes　are　rarely　been　employed　to　discover　the　cause.　In　this　research,　one　proposed　expert-driven　and　seven　automated　feature　selection　schemes　with　five　well-known　machine　learning　classifiers　using　(10　&　30)-fold　cross-validations　are　employed　for　the　establishment　of　an　efficient　and　accurate　LGA　classification　model.　Accuracy,　precision,　and　AUC　scores　are　selected　for　the　evaluation　of　the　proposed　scheme.　Wilcoxon　signed　rank,　friedman,　and　bonferroni-dunn　tests　are　used　to　observe　the　variations　among　(10　&　30)-fold　cross　validation　results　and　to　rank　various　feature　selection　and　classification　schemes.　Two　baseline　methods　are　also　used　to　compare　the　results　of　the　proposed　expert-driven　feature　selection　scheme.　The　top　20　features　selected　by　the　proposed　expert-driven　feature　selection　scheme　outperformed　among　seven　automated　feature　selection　schemes.　A　comparison　analysis　is　also　performed　between　expert-driven　and　data-driven　feature　subsets.　Furthermore,　with　the　intersection　of　proposed　expert-driven　and　data-driven　feature　subsets,　it　is　foreseen　that　out　of　20　features,　11　features　are　found　similar,　which　authenticates　the　proposed　scheme.　The　classification　performance　of　the　11　extracted　features　is　almost　similar　to　the　proposed　expert-driven　feature　selection　scheme.　Ensemble　technique　is　also　exploited　to　build　the　better　and　effective　LGA　classification　model.　©　2020,　Springer　Nature　Singapore　Pte　Ltd.