The Research about Heart Attack Prediction Model

Abstract

Nowadays, coronary heart disease is becoming the most important cause of death all over the world. The use of science technology makes it much more easier for people to analyze the causes for the different kinds of diseases. This article uses the Behavioral Risk Factor Surveillance System to assemble data on health-related risk behaviors from more than 400,000 Americans and analyze these data to provide a precise prediction model for coronary heart disease. The article uses methods like Logistic regression, support vector machine (SVM) and random forest (RF) to explore a better prediction model. After all the calculation, the accuracy of logistic regression is not very high because the accuracy is 85%. SVM and RF proves to make a better prediction since the accuracy of these two models are both above 90% and random forest is the best predictor. To discover how the variables contributes to construct the model, the weight of these variables are crucial. Of all these variables, the proportion of BMI is 44.34%, which has the highest weight and plays a key role in model construction. Moreover, further predictions and better models should be set up to analyze the data. In conclusion, the study shows that SVM and RF make better predictions of heart disease than simple logistic regression. More comprehensive data from more respondents and more suitable analytic models need to use to improve prediction.