2019 The 5th International Conference on Electrical Engineering, Control and Robotics | |
Multilevel Risk Prediction of Cardiovascular Disease based on Adaboost+RF Ensemble Learning | |
无线电电子学;计算机科学 | |
Li, Runchuan^1^2 ; Shen, Shengya^3 ; Chen, Gang^1^2^4 ; Xie, Tiantian^1 ; Ji, Shasha^1 ; Zhou, Bing^1^2 ; Wang, Zongmin^1^2 | |
Industrial Technology Research Institute, Zhengzhou University, Zhengzhou Henan | |
450000, China^1 | |
Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou Henan | |
450000, China^2 | |
School of Foreign Languages, Zhengzhou University, Zhengzhou Henan | |
450000, China^3 | |
School of Distance Learning, Zhengzhou University, Zhengzhou Henan | |
450000, China^4 | |
关键词: Cardio-vascular disease; Contribution degree; Ensemble learning; Information gain ratio; Multiple levels; Prediction model; Risk predictions; Unbalanced datasets; | |
Others : https://iopscience.iop.org/article/10.1088/1757-899X/533/1/012050/pdf DOI : 10.1088/1757-899X/533/1/012050 |
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学科分类:计算机科学(综合) | |
来源: IOP | |
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【 摘 要 】
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.
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