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arXiv · 2111.11477

Prediction Model for Mortality Analysis of Pregnant Women Affected With COVID-19

Quazi Adibur Rahman Adib·Sidratul Tanzila Tasmi·Md. Shahriar Islam Bhuiyan·Md. Mohsin Sarker Raihan·Abdullah Bin Shams
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Abstract

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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Quazi Adibur Rahman Adib, Sidratul Tanzila Tasmi, Md. Shahriar Islam Bhuiyan, Md. Mohsin Sarker Raihan, Abdullah Bin Shams. 2021-11-22. Prediction Model for Mortality Analysis of Pregnant Women Affected With COVID-19. https://doi.org/10.1109/iccit54785.2021.9689824

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