COVID-19 pandemic is an ongoing global pandemic which has caused unprecedented disruptions in the public health sector and global economy. The virus, SARS-CoV-2 is responsible for the rapid transmission of coronavirus disease. Due to its contagious nature, the virus can easily infect an unprotected and exposed individual from mild to severe symptoms. The study of the virus effects on pregnant mothers and neonatal is now a concerning issue globally among civilians and public health workers considering how the virus will affect the mother and the neonates health. This paper aims to develop a predictive model to estimate the possibility of death for a COVID-diagnosed mother based on documented symptoms: dyspnea, cough, rhinorrhea, arthralgia, and the diagnosis of pneumonia. The machine learning models that have been used in our study are support vector machine, decision tree, random forest, gradient boosting, and artificial neural network. The models have provided impressive results and can accurately predict the mortality of pregnant mothers with a given input.The precision rate for 3 models(ANN, Gradient Boost, Random Forest) is 100% The highest accuracy score(Gradient Boosting,ANN) is 95%,highest recall(Support Vector Machine) is 92.75% and highest f1 score(Gradient Boosting,ANN) is 94.66%. Due to the accuracy of the model, pregnant mother can expect immediate medical treatment based on their possibility of death due to the virus. The model can be utilized by health workers globally to list down emergency patients, which can ultimately reduce the death rate of COVID-19 diagnosed pregnant mothers.
翻译:COVID-19大流行是一种持续的全球流行病,对公共卫生部门和全球经济造成了前所未有的破坏。病毒SARS-COV-2是造成冠状病毒疾病迅速传播的原因。由于其传染性质,病毒很容易感染到无防护和暴露的个人,从轻度到严重症状。关于病毒对孕妇和新生儿的影响的研究现已成为全球平民和公共卫生工作者的一个问题,考虑到病毒将如何影响母亲和新生儿健康。本文旨在开发一个预测模型,以估计COVID诊断的母亲死亡的可能性,该模型基于有记录的症状:糖尿病、咳嗽、犀牛、动脉拉动和肺炎诊断。我们研究中使用的机器学习模型是支持病媒机、决策树、随机森林、梯度加速和人工神经网络。模型提供了令人印象深刻的结果,并可以精确地预测孕妇死亡率。三种模型(ANNN、GEST、RMFRF)的精确率可以降低100 %的孕妇死亡率。 孕妇死亡率最高的精确度评分(GNIS75),其最高的精确度比值可以比值可以提高。