We　propose　a　cluster-based　feature　selection　(CFS)　scheme　to　establish　an　efficient　prognosis　process　for　the　identification　of　a　Macrosomia　fetus.　Macrosomia　fetus　adheres　numerous　complications　during　and　after　the　antepartum　period　and　is　among　established　reasons　for　neonate　mortality.　Almost　all　of　the　classifiers　with　the　proposed　CFS　scheme　elevated　macrosomia　prediction　scores　compare　to　previously　published　studies.　The　prediction　scores　are　increased　by　+4%　and　+12%　in　terms　of　precision　and　Area　under　the　curve　which　authenticates　the　applied　scheme.　Therefore,　we　suggest　pediatricians　use　CFS　scheme　with　Support　Vector　Machine　(SVM)　for　developing　better　prognosis　process　to　develop　the　best　macrosomia　prediction　framework.　©　2020,　Springer　Nature　Singapore　Pte　Ltd.