With　the　accelerated　development　of　internet　and　information　technology,　the　healthcare　industry　has　undergone　tremendous　changes.　Big　data,　cloud　computing　and　intelligent　algorithms　are　playing　an　important　role　in　the　medical　field.　Due　to　unbalanced　economic　development,　there　is　a　large　gap　between　medical　care　in　urban　and　rural　areas.　However,　most　patients　with　chronic　disease　live　in　rural　and　remote　areas.　In　order　to　enable　low-income　groups　to　have　access　to　advanced　medical　resources,　this　paper　designs　an　intelligent　healthcare　system　for　chronic　disease　auxiliary　diagnosis.　Taking　heart　disease,　the　most　common　disease　in　daily　life,　as　the　research　object,　an　ensemble　learning　algorithm　based　on　stacking　is　explored　to　predict　the　early　diagnosis　of　heart　disease　for　users.　The　results　show　that　the　ensemble　learning　algorithm　is　better　than　a　single　machine　learning　algorithm,　and　the　accuracy　of　the　prediction　can　be　greatly　improved.　It　is　expected　that　this　can　be　used　to　improve　diagnosis　in　rural　and　remote　areas.　©　2023　IEEE.