Using Bagging Neural Network to Predict the Factors Affecting Neonatal Mortality

IF 1.3 Q3 PEDIATRICS
Somayeh Heshmat Alvandi, M. Ghojazadeh, M. Heidarzadeh, S. Dastgiri, Hooman Nateghian
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引用次数: 0

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.
利用Bagging神经网络预测新生儿死亡率影响因素
新生儿死亡率是社会健康、治疗和发展的主要指标之一。它反映了母亲的营养和生活质量,以及社会为母亲和儿童提供的医疗服务的比率。本研究旨在使用Rapidminer软件中的bagging神经网络来确定影响新生儿死亡率的因素。材料和方法本研究对2015年伊朗全国8053例新生儿(包括1605例死亡病例和6448例对照病例)进行了研究。母亲风险因素、母亲年龄、胎龄、儿童性别、出生体重、出生顺序和先天性畸形等因素被用作bagging神经网络的预测变量。将一些标准,包括ROC曲线下的面积,以及bagging神经网络的性质和灵敏度,与神经网络模型进行了比较。套袋神经网络在预测影响新生儿死亡率的因素方面具有较好的效果,准确率为99.24%。结果我们提出的方法表明,胎龄是新生儿出生时状态的最重要预测因素。此外,1分钟的Apgar、复苏需求、5分钟的Apar、出生体重、先天性畸形和出生顺序,以及母亲的糖尿病和先兆子痫,被确定为胎龄后最重要的预测因素。结论本研究发现的因素可被认为是降低新生儿死亡率的因素。这可以帮助母亲社区的健康,优化医疗服务和社会发展。
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来源期刊
CiteScore
3.90
自引率
0.00%
发文量
0
审稿时长
4 weeks
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.
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