A CVD Predicion Model Based On Optimized Extreme Learning Machine
[1]Lu Peng,Li Qihang,Shang Liga,et al.A CVD Predicion Model Based On Optimized Extreme Learning Machine[J].Journal of Zhengzhou University (Engineering Science),2019,40(02):1-5.[doi:10.13705/j.issn.1671-6833.2018.05.005]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
40卷
Number of periods:
2019 02
Page number:
1-5
Column:
Public date:
2019-03-19
- Title:
- A CVD Predicion Model Based On Optimized Extreme Learning Machine
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2018.05.005
- Abstract:
- Abstract: In order to increase the risk factors that could be accepted and reduce the data format requirements in cardiovascular disease (CVD) predicition models,machine learning algorithms were used to change the strict mathematical formulas of traditional CVD prediction models.Frstly,a CVD prediction model by extreme learning machine (ELM)algorithm based on single hidden layer feed-forward neural network (SLFNs) was proposed. Further more,an enhanced leader particle swarm optimization(ELPSO) through a five-staged successive mutation method was used ,and the optimized strategy of PSO was also used to optimize the SLFNs hidden layer units parameters.The analysis results on Statlog(Heart) dataset and Heart Disease Dataset of UCI database indicated that the test accuracy of proposed ELPSO-ELM model could reach 85.71%and 84.00%respectively, the AUC(The area under the ROC curve )could reach 0.9024 and 0.8423 respectively.They were higher than conventional CVD prediction models.The proposed models relaxed the linear constraints of data format and more complex risk factors could be accepted.
- References:
- -
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Last Update:
2019-03-21