Heart　diseases　are　commonly　diagnosed　by　using　the　technique　of;　angiography　which　is　time　consuming　procedure.　Therefore,　researchers　have　been　encouraged　to　find　out　the　alternatives　like;　machine　learning　(ML).　In　this　research　work,　we　present　a　method　to　predict　the　heart　disease　using　correlation　based　on　feature　subset　selection　and　sampling.　Different　ML　algorithms　have　been　used　in　this　research　to　model　and　cross　validate.　The　dataset　consists　of　14　features　and　1000　records　of　heart　disease　patients.　Two　sampling　techniques;　Oversampling　and　Synthetic　Minority　Optimization　Technique　(SMOTE)　have　been　used.　In　case　of　oversampling,　the　Random　Forest　(RF)　algorithm　achieved　highest　prediction　accuracy　i.e.,　99.20%.　Proposed　model　with　SMOTE　improved　the　prediction　accuracy　of　all　algorithms　to　100%　except　k-nn,　which　is　also　improved　18.16%.　In　this　study　the　features　selection　has　been　made　on　the　basis　of　correlation,　in　future　we　plan　to　tap　in　other　feature　selection　methodologies.　Furthermore　in　this　study　cross　validation　have　been　used　to　address　the　issue　of　over-fitting,　this　work　can　be　extended　by　using　other　techniques　like;　Federated　Learning　to　address　the　subjected　problem.　©　2023　IEEE.