Prediction of fetal weight based on back propagation neural network optimized by genetic algorithm
Fetal weight is an important index to judge fetal development and ensure the safety of pregnant women. However, fetal weight cannot be directly measured. This study proposed a prediction model of fetal weight based on genetic algorithm to optimize back propagation (GA-BP) neural network. Using rando...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2021-05-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | http://www.aimspress.com/article/doi/10.3934/mbe.2021222?viewType=HTML |
| _version_ | 1819079568925917184 |
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| author | Hong Gao Cuiyun Wu Dunnian Huang Dahui Zha Cuiping Zhou |
| author_facet | Hong Gao Cuiyun Wu Dunnian Huang Dahui Zha Cuiping Zhou |
| author_sort | Hong Gao |
| collection | DOAJ |
| description | Fetal weight is an important index to judge fetal development and ensure the safety of pregnant women. However, fetal weight cannot be directly measured. This study proposed a prediction model of fetal weight based on genetic algorithm to optimize back propagation (GA-BP) neural network. Using random number table method, 80 cases of pregnant women in our hospital from September 2018 to March 2019 were divided into control group and observation group, 40 cases in each group. The doctors in the control group predicted the fetal weight subjectively according to routine ultrasound and physical examination. In the observation group, the continuous weight change model of pregnant women was established by using the regression model and the historical physical examination data obtained by feature normalization pretreatment, and then the genetic algorithm (GA) was used to optimize the initial weights and thresholds of back propagation (BP) neural network to establish the fetal weight prediction model. The coincidence rate of fetal weight was compared between the two groups after birth. Results: The prediction error of GA-BPNN was controlled within 6%. And the accuracy of GA-BPNN was 76.3%, which were 14.5% higher than that of traditional methods. According to the error curve, GA-BP is more effective in predicting the actual fetal weight. Conclusion: The GA-BPNN model can accurately and quickly predict fetal weight. |
| first_indexed | 2024-12-21T19:31:04Z |
| format | Article |
| id | doaj.art-73f6a3d4a90c47b78c48ca45a760e0b3 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-21T19:31:04Z |
| publishDate | 2021-05-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-73f6a3d4a90c47b78c48ca45a760e0b32022-12-21T18:52:42ZengAIMS PressMathematical Biosciences and Engineering1551-00182021-05-011844402441010.3934/mbe.2021222Prediction of fetal weight based on back propagation neural network optimized by genetic algorithmHong Gao0Cuiyun Wu 1Dunnian Huang 2Dahui Zha3Cuiping Zhou4The Third People's Hospital of HeFei, Heifei 230000, ChinaThe Third People's Hospital of HeFei, Heifei 230000, ChinaThe Third People's Hospital of HeFei, Heifei 230000, ChinaThe Third People's Hospital of HeFei, Heifei 230000, ChinaThe Third People's Hospital of HeFei, Heifei 230000, ChinaFetal weight is an important index to judge fetal development and ensure the safety of pregnant women. However, fetal weight cannot be directly measured. This study proposed a prediction model of fetal weight based on genetic algorithm to optimize back propagation (GA-BP) neural network. Using random number table method, 80 cases of pregnant women in our hospital from September 2018 to March 2019 were divided into control group and observation group, 40 cases in each group. The doctors in the control group predicted the fetal weight subjectively according to routine ultrasound and physical examination. In the observation group, the continuous weight change model of pregnant women was established by using the regression model and the historical physical examination data obtained by feature normalization pretreatment, and then the genetic algorithm (GA) was used to optimize the initial weights and thresholds of back propagation (BP) neural network to establish the fetal weight prediction model. The coincidence rate of fetal weight was compared between the two groups after birth. Results: The prediction error of GA-BPNN was controlled within 6%. And the accuracy of GA-BPNN was 76.3%, which were 14.5% higher than that of traditional methods. According to the error curve, GA-BP is more effective in predicting the actual fetal weight. Conclusion: The GA-BPNN model can accurately and quickly predict fetal weight.http://www.aimspress.com/article/doi/10.3934/mbe.2021222?viewType=HTMLfetal weightback propagationneural networkgenetic algorithmprediction model |
| spellingShingle | Hong Gao Cuiyun Wu Dunnian Huang Dahui Zha Cuiping Zhou Prediction of fetal weight based on back propagation neural network optimized by genetic algorithm Mathematical Biosciences and Engineering fetal weight back propagation neural network genetic algorithm prediction model |
| title | Prediction of fetal weight based on back propagation neural network optimized by genetic algorithm |
| title_full | Prediction of fetal weight based on back propagation neural network optimized by genetic algorithm |
| title_fullStr | Prediction of fetal weight based on back propagation neural network optimized by genetic algorithm |
| title_full_unstemmed | Prediction of fetal weight based on back propagation neural network optimized by genetic algorithm |
| title_short | Prediction of fetal weight based on back propagation neural network optimized by genetic algorithm |
| title_sort | prediction of fetal weight based on back propagation neural network optimized by genetic algorithm |
| topic | fetal weight back propagation neural network genetic algorithm prediction model |
| url | http://www.aimspress.com/article/doi/10.3934/mbe.2021222?viewType=HTML |
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