Prediction Model for Mortality Analysis of Pregnant Women Affected With COVID-19. (arXiv:2111.11477v1 [cs.LG])

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.



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