Optimization Of Infant Birth Predictions During The Covid-19 Pandemic Using The Particle Swarm Optimization Based K-Nn Algorithm Method

2022 International Seminar on Application for Technology of Information and Communication (iSemantic) Pub Date : 2022-09-17 DOI:10.1109/iSemantic55962.2022.9920468

Ayu Hernita, M. Soeleman, A. Zainul Fanani

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引用次数: 1

Abstract

Every mother wants to give birth to a perfect and healthy child. many things cause newborns to die, some of which are malnutrition during the womb, fetuses that have abnormalities in the body, and factors of premature birth. Deaths due to exposure to the Covid-19 virus are certainly a serious problem. Several factors influence childbirth, such as placental and fetal factors, maternal factors, lifestyle factors, and what is happening now due to the covid-19 virus. Therefore, the author is interested and wants to review to find out the characteristics of mothers who give birth due to exposure to the covid virus and are normal. The results of tests carried out by optimizing the Particle Swarm Optimization-based K-NN Algorithm resulted in an accuracy value of 93%. The accuracy value can be said to be good enough to determine the characteristics of the mother who gave birth under normal or premature conditions.

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基于粒子群优化的K-Nn算法优化Covid-19大流行期间婴儿出生预测

每个母亲都想生一个完美健康的孩子。导致新生儿死亡的原因很多，其中一些是子宫内营养不良，胎儿体内有异常，以及早产的因素。暴露于Covid-19病毒导致的死亡当然是一个严重的问题。有几个因素影响分娩，如胎盘和胎儿因素、孕产妇因素、生活方式因素，以及由于covid-19病毒现在正在发生的事情。因此，作者很感兴趣，想要回顾一下因感染新冠病毒而分娩的母亲的特征，这些母亲是正常的。通过优化基于粒子群优化的K-NN算法进行的测试结果表明，准确率达到93%。准确度值可以说是足够好的，可以判断在正常或早产条件下分娩的母亲的特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 International Seminar on Application for Technology of Information and Communication (iSemantic)

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