基于GA-BP神经网络的胎儿体重预测分析
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摘要
胎儿体重是判断胎儿发育、保障孕产妇安全的重要指标,但是胎儿体重无法直接测得,只能根据孕妇体检数据进行预测.提出了一种基于遗传算法优化BP神经网络(GA-BPNN)的胎儿体重预测模型,首先采用回归模型和特征归一化预处理得到的历史体检数据建立孕妇连续体重变化模型,然后利用遗传算法(Genetic Algorithm,GA)优化BP神经网络的初始权值和阈值,建立胎儿体重预测模型.从我国东部某医院2016年孕产妇中随机抽取3000例样本数据,将本文的模型与基于传统BP神经网络(BPNN)的预测模型进行比较,实验结果表明,本文提出的GA-BPNN胎儿体重预测模型不仅加快了模型的收敛速度,而且将胎儿体重预测精度提高了14%.
Fetal weight is an important indicator of fetal development and maternal safety,but fetal weight cannot be measured directly and can only be predicted according to the examination data of pregnant women.This study proposes a model of fetal weight prediction based on the Genetic Algorithm to optimize BP Neural Network(GA-BPNN).First,the model of continuous weight change in pregnant women is established by using regression model and feature normalization preprocessing.Then,the genetic algorithm is used to optimize the initial weights and thresholds of BP neural network and establish a fetal weight prediction model.3000 pregnant women data are randomly sampled from a hospital in the eastern part of China in 2016.The proposed model is compared with the prediction model based on the traditional BP neural network.The results show that the GA-BPNN fetal weight prediction model proposed in this paper not only accelerates the convergence of the model,but also improves the prediction accuracy of fetal weight by 14%.
Fetal weight is an important indicator of fetal development and maternal safety,but fetal weight cannot be measured directly and can only be predicted according to the examination data of pregnant women.This study proposes a model of fetal weight prediction based on the Genetic Algorithm to optimize BP Neural Network(GA-BPNN).First,the model of continuous weight change in pregnant women is established by using regression model and feature normalization preprocessing.Then,the genetic algorithm is used to optimize the initial weights and thresholds of BP neural network and establish a fetal weight prediction model.3000 pregnant women data are randomly sampled from a hospital in the eastern part of China in 2016.The proposed model is compared with the prediction model based on the traditional BP neural network.The results show that the GA-BPNN fetal weight prediction model proposed in this paper not only accelerates the convergence of the model,but also improves the prediction accuracy of fetal weight by 14%.
引文
1刘致君,李桂荣,郭兴巧.预测胎儿体重新方法与传统方法的比较.中国妇幼保健,2008,23(24):3478–3479.[doi:10 .3969/j.issn.1001-4411.2008.24.065]
2Yu ZB,Han SP,Zhu JG,et al.Pre-pregnancy body mass index in relation to infant birth weight and offspring overweight/obesity:A systematic review and meta-analysis.PLo S One,2013,8(4):e61627.[doi:10.1371/journal.pone.0061627]
3Shepard MJ,Richards VA,Berkowitz RL,et al.An evaluation of two equations for predicting fetal weight by ultrasound.American Journal of Obstetrics and Gynecology,1982,142(1):47–54.[doi:10.1016/S0002-9378(16)32283-9]
4Hadlock FP,Harrist RB,Carpenter RJ,et al.Sonographic estimation of fetal weight.The value of femur length in addition to head and abdomen measurements.Radiology,1984,150(2):535–540.[doi:10.1148/radiology.150.2.6691115]
5朱桐梅,赵晓华,艾梅,等.6种预测胎儿体重公式准确性的对比研究.中国妇幼保健,2016,31(20):4179–4181.
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7洪传美,纪毅梅.胎儿体重预测常见方法比较及临床价值探讨.中国妇幼健康研究,2017,28(5):522–523,530.
8刁晓娣,江志斌,刘瑾.根据孕妇参数预测胎儿体重的神经网络方法.中国生物医学工程学报,1999,18(2):155–158,193.
9Farmer RM,Medearis AL,Hirata GI,et al.The use of a neural network for the ultrasonographic estimation of fetal weight in the macrosomic fetus.American Journal of Obstetrics and Gynecology,1992,166(5):1467–1472.[doi:10 .1016/0002-9378(92)91621-G]
10Cheng YC,Hsia CC,Chang FM,et al.Cluster-based artificial neural network on ultrasonographic parameters for fetal weight estimation.6th World Congress of Biomechanics(WCB 2010).Singapore.2010.1514–1517.
11Mohammadi H,Nemati M,Allahmoradi Z,et al.Ultrasound estimation of fetal weight in twins by artificial neural network.Journal of Biomedical Science and Engineering,2011,4(1):46–50.[doi:10.4236/jbise.2011.41006]
12李昆,柴玉梅,赵红领,等.基于深度神经网络的胎儿体重预测.计算机科学,2016,43(11A):73–76,82.
13Chen GY,Fu KY,Liang ZW,et al.The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process.Fuel,2014,(126):202–212.[doi:10 .1016/j.fuel.2014.02.034]
14Rumelhart DE,Hinton GE,Williams RJ.Learning representations by back-propagating errors.Nature,1986,323 (6088):533–536.[doi:10.1038/323533a0]
15Krizhevsky A,Sutskever I,Hinton GE.Imagenet classification with deep convolutional neural networks.Proceedings of the25 th International Conference on Neural Information Processing Systems.Lake Tahoe,NV,USA.2012.1097–1105.
16杨启文,蒋静坪,张国宏.遗传算法优化速度的改进.软件学报,2001,12(2):270–275.
17难产与围产编写组.难产与围产.重庆:科学技术文献出版社重庆分社,1983.
2Yu ZB,Han SP,Zhu JG,et al.Pre-pregnancy body mass index in relation to infant birth weight and offspring overweight/obesity:A systematic review and meta-analysis.PLo S One,2013,8(4):e61627.[doi:10.1371/journal.pone.0061627]
3Shepard MJ,Richards VA,Berkowitz RL,et al.An evaluation of two equations for predicting fetal weight by ultrasound.American Journal of Obstetrics and Gynecology,1982,142(1):47–54.[doi:10.1016/S0002-9378(16)32283-9]
4Hadlock FP,Harrist RB,Carpenter RJ,et al.Sonographic estimation of fetal weight.The value of femur length in addition to head and abdomen measurements.Radiology,1984,150(2):535–540.[doi:10.1148/radiology.150.2.6691115]
5朱桐梅,赵晓华,艾梅,等.6种预测胎儿体重公式准确性的对比研究.中国妇幼保健,2016,31(20):4179–4181.
6 M?st L,Schmid M,Faschingbauer F,et al.Predicting birth weight with conditionally linear transformation models.Statistical Methods in Medical Research,2016,25(6):2781–2810.[doi:10.1177/0962280214532745]
7洪传美,纪毅梅.胎儿体重预测常见方法比较及临床价值探讨.中国妇幼健康研究,2017,28(5):522–523,530.
8刁晓娣,江志斌,刘瑾.根据孕妇参数预测胎儿体重的神经网络方法.中国生物医学工程学报,1999,18(2):155–158,193.
9Farmer RM,Medearis AL,Hirata GI,et al.The use of a neural network for the ultrasonographic estimation of fetal weight in the macrosomic fetus.American Journal of Obstetrics and Gynecology,1992,166(5):1467–1472.[doi:10 .1016/0002-9378(92)91621-G]
10Cheng YC,Hsia CC,Chang FM,et al.Cluster-based artificial neural network on ultrasonographic parameters for fetal weight estimation.6th World Congress of Biomechanics(WCB 2010).Singapore.2010.1514–1517.
11Mohammadi H,Nemati M,Allahmoradi Z,et al.Ultrasound estimation of fetal weight in twins by artificial neural network.Journal of Biomedical Science and Engineering,2011,4(1):46–50.[doi:10.4236/jbise.2011.41006]
12李昆,柴玉梅,赵红领,等.基于深度神经网络的胎儿体重预测.计算机科学,2016,43(11A):73–76,82.
13Chen GY,Fu KY,Liang ZW,et al.The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process.Fuel,2014,(126):202–212.[doi:10 .1016/j.fuel.2014.02.034]
14Rumelhart DE,Hinton GE,Williams RJ.Learning representations by back-propagating errors.Nature,1986,323 (6088):533–536.[doi:10.1038/323533a0]
15Krizhevsky A,Sutskever I,Hinton GE.Imagenet classification with deep convolutional neural networks.Proceedings of the25 th International Conference on Neural Information Processing Systems.Lake Tahoe,NV,USA.2012.1097–1105.
16杨启文,蒋静坪,张国宏.遗传算法优化速度的改进.软件学报,2001,12(2):270–275.
17难产与围产编写组.难产与围产.重庆:科学技术文献出版社重庆分社,1983.