Comparison of Classification Data Mining Models Predicting Heart Disease in Europe

Abstract

Heart disease is the no.1 killer disease globally, and it has become a scary thing for the whole world is, no exception in Europe. Data obtained from the World Health Organization in 2020, nearly 10 million people died, while by 2030, it is predicted to reach 50 million per year. In Europe, the death rate from heart disease spread in various countries such as France, which reached 213 deaths per million population, Spain with 481 million deaths, and the Netherlands with about 491 population deaths. The study provided a classification model of heart disease predictions by comparing the best random forest methods with naive Bayes. The results of this study if the decision making of the best model depends on the needs that occur in various European countries. For example, the northern European states, especially Scandinavia such as Denmark, Sweden and other European countries that are characterized by light brown and dark brown colors that have a heart disease value above 2,000 are recommended to use a classification model with the Naive Bayes method because, while for countries that are light blue with dark blue with an infected value below 2,000 it is recommended to use the Random forest method.