Prediction of cardiovascular disease survival based on artificial neural network

Cardiovascular disease (CVD) is a kind of chronic disease involving the heart and blood vessels. In the early 21st century, cardiovascular diseases accounted for nearly 50% of the mortality in developed countries and about 25% in developing countries, and cardiovascular diseases have gradually become common diseases. Accurate prediction of survival events of patients with cardiovascular disease can provide more meaningful reference for subsequent treatment, and strive for the best treatment opportunity for patients to achieve the purpose of prolonging life. The data set collected by Kaggle was used in this study, which included variables such as high blood pressure, creatinine phosphokinase, ejection fraction, serum creatinine, and smoking. Based on Akaike information criterion (AIC), stepwise regression analysis was used to select the strongly correlated variables of cardiovascular disease, and then an artificial neural network (ANN) based survival prediction model of cardiovascular disease was constructed. In this paper, support vector machine (SVM) and naive Bayes are used to compare with the artificial neural network. The results show that the performance of artificial neural network is better than other algorithms regardless of the use of strongly correlated variables. After using strongly correlated variables, the performance of each algorithm is improved. After training the artificial neural network with strong correlation variables, it has the highest accuracy, accuracy, recall rate, F1-score and AUC, which can reach 0.81, 0.83, 0.85, 0.84 and 0.84 respectively.