Cardiac Arrest Prediction Using Gradient Boosting Algorithm

The main killer in the globe, according to the WHO, is cardiovascular disease. Almost 12 million people die as a result of it worldwide each year. The major problem that needs to be resolved is that one must be told sufficiently in advance to take precautions. As a result, this paper describes a strategy for cardiac arrest prediction that is exclusively based on Gradient boosting classifiers, logistic regression, and also classifiers from random forests. The accuracy and examination take into account rates for this ensemble version were each 73 percent. By taking into account a person's height, weight, age, systolic and diastolic blood pressure, cholesterol levels, smoking and drinking habits, as well as dietary and lifestyle choices, the paper attempt to establish how likely it is that they will experience a cardiac arrest. Also, it provides consumers with a cloud-deployed web tool that they can use to input these facts and find out their risk of having a heart attack.