This　study　is　based　on　baseline　data　and　2-year　follow-up　information　from　145　heart　failure　patients　in　Guangxi,　China,　combined　with　a　publicly　available　scientific　dataset　on　Chinese　heart　failure　patients.　Multiple　datasets　of　varying　scales　were　constructed,　and　traditional　Cox　proportional　hazards　models,　along　with　Logistic　Regression,　Random　Forest,　Support　Vector　Machine,　XGBoost,　Random　Survival　Forest,　and　Survival　Support　Vector　Machine　algorithms　were　employed　to　develop　heart　failure　mortality　risk　prediction　models.　These　models　were　used　to　identify　and　quantitatively　evaluate　both　risk　factors　and　protective　factors　for　HF　mortality.　In　terms　of　the　outcome,　XGBoost　demonstrated　superior　performance　on　high-dimensional　datasets　with　missing　values,　whereas　Support　Vector　Machine　exhibited　stronger　predictive　capability　on　similarly　scaled　datasets　without　missing　values.　The　results　based　on　XGBoost　model　and　evaluation　based　on　SHAP　values　further　confirmed　that　Glomerular　Filtration　Rate,　Height　and　Glucose　are　critical　predictors.　For　survival　time　analysis,　the　Cox　models　generally　outperformed　Random　Survival　Forest　and　Survival　Support　Vector　Machine　algorithms.　The　mutual　validation　of　different　modeling　approaches　can　enhance　the　robustness　and　effectiveness　of　heart　failure　mortality　risk　prediction,　better　supporting　clinical　prevention　and　treatment　decision-making.　©　2024　IEEE.