Multilevel Risk Prediction of Cardiovascular Disease based on Adaboost+RF Ensemble Learning

Abstract

Abstract Background: In the field of diagnostic CVD, the predecessors used a large amount of data with no missing two-category data, and obtained good results. However, in the process of electronic input of historical data, a large number of data attribute values are missing, and there are multiple levels of disease risk. Goal: On the data set of imbalance and a large number of missing values, this paper focuses on the five levels of cardiovascular disease. Methods: A new prediction model of Adaboost+RF is constructed by using the information gain ratio to analyze the feature contribution degree of the data set. The performance of this model is evaluated with Precision, Recall, F-measure and ROC Area values. Results: The results show that the four key indicators of the Adaboost+RF model on five-categories unbalanced datasets in Precision, Recall, F1 and AUC values, which are 40.9%, 49.3%, 41.4% and 71.6%. Conclusion: The experiment results demonstrate that the four key indicators of the Adaboost+RF model on five-category unbalanced missing datasets are better than other machine learning algorithms.