Predict heart failure using ML

Background

Most of the models created for modeling survival for heart failure are only moderately accurate, and the predictive factors have limited interpretability. Recent models have shown improvements, particularly when the survival outcome is combined with additional targets (for example, hospitalization). Despite the fact that scientists have identified a large number of predictors and indicators, there is no consensus on their relative importance in predicting survival 1.
The app uses the dataset from this study.
The variables used are Age, Anaemia, High blood pressure, Creatinine phosphokinase, Diabetes, Ejection fraction, Sex, Platelets, Serum creatinine, Serum sodium, Smoking, Time and the target (death event). The authors find that Random forest is the best algorithm, which corroborates my results.

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