Somayeh Heshmat Alvandi, M. Ghojazadeh, M. Heidarzadeh, S. Dastgiri, Hooman Nateghian
{"title":"利用Bagging神经网络预测新生儿死亡率影响因素","authors":"Somayeh Heshmat Alvandi, M. Ghojazadeh, M. Heidarzadeh, S. Dastgiri, Hooman Nateghian","doi":"10.22038/IJP.2021.57861.4538","DOIUrl":null,"url":null,"abstract":"BackgroundThe rate of neonatal mortality is one of the main indices of health, treatment, and development in societies. It reflects the quality of nutrition and life of mothers as well as the rate of healthcare services that mothers and children are provided with by societies. This study aimed to identify the factors affecting neonatal mortality by using a bagging neural network in Rapidminer Software. Materials and MethodsThe study was conducted on 8053 births (including 1605 death cases and 6448 control cases) all over Iran in 2015. Factors such as maternal risk factors, mother’s age, gestational age, child gender, birth weight, birth order, and congenital anomalies were utilized as the predictor variables of the bagging neural network. Some criteria including the area under the ROC curve, as well as the property and sensitivity of the bagging neural network, were compared with the neural network model. And the bagging neural network with 99.24% precision rate enjoyed better results in predicting those factors affecting neonatal mortality.ResultsOur suggested method revealed that gestational age is the most significant predictor factor of a neonate's status at birth time. Besides, 1-minute Apgar, need for resuscitation, 5-minute Apgar, birth weight, congenital anomalies, and birth order, as well as diabetes and preeclampsia in mothers, were identified as the most significant predictor factors after the gestational age.ConclusionFactors discovered in this study can be considered to decrease neonatal mortality. This can help the health of mothers’ community, optimize healthcare services, and development of societies.","PeriodicalId":51591,"journal":{"name":"International Journal of Pediatrics","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using Bagging Neural Network to Predict the Factors Affecting Neonatal Mortality\",\"authors\":\"Somayeh Heshmat Alvandi, M. Ghojazadeh, M. Heidarzadeh, S. Dastgiri, Hooman Nateghian\",\"doi\":\"10.22038/IJP.2021.57861.4538\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BackgroundThe rate of neonatal mortality is one of the main indices of health, treatment, and development in societies. It reflects the quality of nutrition and life of mothers as well as the rate of healthcare services that mothers and children are provided with by societies. This study aimed to identify the factors affecting neonatal mortality by using a bagging neural network in Rapidminer Software. Materials and MethodsThe study was conducted on 8053 births (including 1605 death cases and 6448 control cases) all over Iran in 2015. Factors such as maternal risk factors, mother’s age, gestational age, child gender, birth weight, birth order, and congenital anomalies were utilized as the predictor variables of the bagging neural network. Some criteria including the area under the ROC curve, as well as the property and sensitivity of the bagging neural network, were compared with the neural network model. And the bagging neural network with 99.24% precision rate enjoyed better results in predicting those factors affecting neonatal mortality.ResultsOur suggested method revealed that gestational age is the most significant predictor factor of a neonate's status at birth time. Besides, 1-minute Apgar, need for resuscitation, 5-minute Apgar, birth weight, congenital anomalies, and birth order, as well as diabetes and preeclampsia in mothers, were identified as the most significant predictor factors after the gestational age.ConclusionFactors discovered in this study can be considered to decrease neonatal mortality. This can help the health of mothers’ community, optimize healthcare services, and development of societies.\",\"PeriodicalId\":51591,\"journal\":{\"name\":\"International Journal of Pediatrics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2021-10-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Pediatrics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.22038/IJP.2021.57861.4538\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PEDIATRICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Pediatrics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.22038/IJP.2021.57861.4538","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PEDIATRICS","Score":null,"Total":0}
Using Bagging Neural Network to Predict the Factors Affecting Neonatal Mortality
BackgroundThe rate of neonatal mortality is one of the main indices of health, treatment, and development in societies. It reflects the quality of nutrition and life of mothers as well as the rate of healthcare services that mothers and children are provided with by societies. This study aimed to identify the factors affecting neonatal mortality by using a bagging neural network in Rapidminer Software. Materials and MethodsThe study was conducted on 8053 births (including 1605 death cases and 6448 control cases) all over Iran in 2015. Factors such as maternal risk factors, mother’s age, gestational age, child gender, birth weight, birth order, and congenital anomalies were utilized as the predictor variables of the bagging neural network. Some criteria including the area under the ROC curve, as well as the property and sensitivity of the bagging neural network, were compared with the neural network model. And the bagging neural network with 99.24% precision rate enjoyed better results in predicting those factors affecting neonatal mortality.ResultsOur suggested method revealed that gestational age is the most significant predictor factor of a neonate's status at birth time. Besides, 1-minute Apgar, need for resuscitation, 5-minute Apgar, birth weight, congenital anomalies, and birth order, as well as diabetes and preeclampsia in mothers, were identified as the most significant predictor factors after the gestational age.ConclusionFactors discovered in this study can be considered to decrease neonatal mortality. This can help the health of mothers’ community, optimize healthcare services, and development of societies.
期刊介绍:
International Journal of Pediatrics is a peer-reviewed, open access journal that publishes original researcharticles, review articles, and clinical studies in all areas of pediatric research. The journal accepts submissions presented as an original article, short communication, case report, review article, systematic review, or letter to the editor.