Prediction Performance Comparison of Risk Management and Control Mode in Regional Sites Based on Decision Tree and Neural Network
The traditional risk management and control mode (RMCM) in regional sites has the defects of low efficiency, high cost, and lack of systematism. Trying to resolve these defects and explore the application possibility of machine learning, a characteristic dataset for RMCM in regional sites was establ...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.892423/full |
| _version_ | 1811255400014544896 |
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| author | Wenhui Zhu Jun He Hongzhen Zhang Liang Cheng Xintong Yang Xiahui Wang Guohua Ji |
| author_facet | Wenhui Zhu Jun He Hongzhen Zhang Liang Cheng Xintong Yang Xiahui Wang Guohua Ji |
| author_sort | Wenhui Zhu |
| collection | DOAJ |
| description | The traditional risk management and control mode (RMCM) in regional sites has the defects of low efficiency, high cost, and lack of systematism. Trying to resolve these defects and explore the application possibility of machine learning, a characteristic dataset for RMCM in regional sites was established. Three decision tree (DT) algorithms (CHAID, EXHAUSTIVE CHAID, and CART) and two artificial neural network (ANN) algorithms [back propagation (BP) and radial basis function (RBF)] were implemented to predict RMCM in regional sites. The results showed that in the aspects of accuracy (ACC), precision (PRE), recall ratio (REC), and F1 value, CART–DT was superior to CHAID–DT and EXHAUSTIVE CHAID–DT (E-CHAID–DT); and BP–ANN was superior to RBF–ANN. However, CART–DT was inferior to BP–ANN in ACC, PRE, REC, and F1 value. BP–ANN model is good at non-linear mapping, and it has a flexible network structure and a low risk of over-fitting. The case study of a typical county demonstration area confirmed the extensibility of the method, and the method has great potential in RMCM prediction in regional sites in the future. |
| first_indexed | 2024-04-12T17:23:14Z |
| format | Article |
| id | doaj.art-2942a98fc7674f308f9d9e44cc74b482 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-12T17:23:14Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-2942a98fc7674f308f9d9e44cc74b4822022-12-22T03:23:24ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-05-011010.3389/fpubh.2022.892423892423Prediction Performance Comparison of Risk Management and Control Mode in Regional Sites Based on Decision Tree and Neural NetworkWenhui ZhuJun HeHongzhen ZhangLiang ChengXintong YangXiahui WangGuohua JiThe traditional risk management and control mode (RMCM) in regional sites has the defects of low efficiency, high cost, and lack of systematism. Trying to resolve these defects and explore the application possibility of machine learning, a characteristic dataset for RMCM in regional sites was established. Three decision tree (DT) algorithms (CHAID, EXHAUSTIVE CHAID, and CART) and two artificial neural network (ANN) algorithms [back propagation (BP) and radial basis function (RBF)] were implemented to predict RMCM in regional sites. The results showed that in the aspects of accuracy (ACC), precision (PRE), recall ratio (REC), and F1 value, CART–DT was superior to CHAID–DT and EXHAUSTIVE CHAID–DT (E-CHAID–DT); and BP–ANN was superior to RBF–ANN. However, CART–DT was inferior to BP–ANN in ACC, PRE, REC, and F1 value. BP–ANN model is good at non-linear mapping, and it has a flexible network structure and a low risk of over-fitting. The case study of a typical county demonstration area confirmed the extensibility of the method, and the method has great potential in RMCM prediction in regional sites in the future.https://www.frontiersin.org/articles/10.3389/fpubh.2022.892423/fulldecision tree (DT)artificial neural network (ANN)regional sitesrisk management and control mode (RMCM)prediction performance |
| spellingShingle | Wenhui Zhu Jun He Hongzhen Zhang Liang Cheng Xintong Yang Xiahui Wang Guohua Ji Prediction Performance Comparison of Risk Management and Control Mode in Regional Sites Based on Decision Tree and Neural Network Frontiers in Public Health decision tree (DT) artificial neural network (ANN) regional sites risk management and control mode (RMCM) prediction performance |
| title | Prediction Performance Comparison of Risk Management and Control Mode in Regional Sites Based on Decision Tree and Neural Network |
| title_full | Prediction Performance Comparison of Risk Management and Control Mode in Regional Sites Based on Decision Tree and Neural Network |
| title_fullStr | Prediction Performance Comparison of Risk Management and Control Mode in Regional Sites Based on Decision Tree and Neural Network |
| title_full_unstemmed | Prediction Performance Comparison of Risk Management and Control Mode in Regional Sites Based on Decision Tree and Neural Network |
| title_short | Prediction Performance Comparison of Risk Management and Control Mode in Regional Sites Based on Decision Tree and Neural Network |
| title_sort | prediction performance comparison of risk management and control mode in regional sites based on decision tree and neural network |
| topic | decision tree (DT) artificial neural network (ANN) regional sites risk management and control mode (RMCM) prediction performance |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.892423/full |
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