Developed a model using ensemble Learning algorithms to predict a person's Death due to heart failure using some essential features ,using the heart-failure-clinical-data set easily available on Kaggle.
Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure. The features are :-
- Age ,Gender , blood pressure, smoke, diabetes,ejection fraction, creatinine phosphokinase, serum_creatinine, serum_sodium, time
Tried building the model with a number of classifiers (Random Forests,Decision Trees,XGB) and sticking with the one which gives the best results.