Background:　With　the　prevalence　of　cardiovascular　diseases　increasing　worldwide,　early　prediction　and　accurate　assessment　of　heart　failure　(HF)　risk　are　crucial　to　meet　the　clinical　demand.　Objective:　Our　study　objective　was　to　develop　machine　learning　(ML)　models　based　on　real-world　electronic　health　records　to　predict　1-year　in-hospital　mortality,　use　of　positive　inotropic　agents,　and　1-year　all-cause　readmission　rate.　Methods:　For　this　single-center　study,　we　recruited　patients　with　newly　diagnosed　HF　hospitalized　between　December　2010　and　August　2018　at　the　First　Affiliated　Hospital　of　Dalian　Medical　University　(Liaoning　Province,　China).　The　models　were　constructed　for　a　population　set　(90:10　split　of　data　set　into　training　and　test　sets)　using　79　variables　during　the　first　hospitalization.　Logistic　regression,　support　vector　machine,　artificial　neural　network,　random　forest,　and　extreme　gradient　boosting　models　were　investigated　for　outcome　predictions.　Results:　Of　the　13,602　patients　with　HF　enrolled　in　the　study,　537　(3.95%)　died　within　1　year　and　2779　patients　(20.43%)　had　a　history　of　use　of　positive　inotropic　agents.　ML　algorithms　improved　the　performance　of　predictive　models　for　1-year　in-hospital　mortality　(areas　under　the　curve　[AUCs]　0.92-1.00),　use　of　positive　inotropic　medication　(AUCs　0.85-0.96),　and　1-year　readmission　rates　(AUCs　0.63-0.96).　A　decision　tree　of　mortality　risk　was　created　and　stratified　by　single　variables　at　levels　of　high-sensitivity　cardiac　troponin　I　(＜0.068　mu　g/L),　followed　by　percentage　of　lymphocytes　(＜14.688%)　and　neutrophil　count　(4.870x10(9)/L).　Conclusions:　ML　techniques　based　on　a　large　scale　of　clinical　variables　can　improve　outcome　predictions　for　patients　with　HF.　The　mortality　decision　tree　may　contribute　to　guiding　better　clinical　risk　assessment　and　decision　making.