Prediction of ICU Admission Analysis for Covid-19 Patients

Abstract

Abstract: A rapid spread of SARS COVID-19 disease was first observed in China since early January 2020 and then in Italy. Covid 19 spread rapidly spread amongst many countries from one person to another person the deaths due to the pandemic continued to increase as all the countries faced many problems due to rapid spread of Coronavirus the health department was also clueless on how to provide facilities to the patients who needed ICU emergency beds in the hospitals as the cases increased all over the world. The SARS coronavirus has infected more than 100 million people and has resulted in almost three million deaths worldwide. In this project, the development of Machine Learning (ML) models for COVID-19 progression is discussed. As the pandemic situation was increased this Machine Learning model builds a web application framework where using the previous datasets we can create a complete blood count data based on which we will be able to the analysis of the ICU admitted patients. The proposed system is a Machine Learning approach which predicts the analysis of ICU and non ICU Patients. The analysis is made by uploaded details of patient which is based on CBC data only. The details of the patient data are data preprocessed and trained so that the system can predict the analysis of the entered data and gives the graphical representation through graphs. The proposed approach is a web application framework using Html, CSS, JavaScript as front end technology and for the validations. Python is an object oriented programming language which is the sever side language in this project. The data visualization is given by considering the analysis of the prediction in ICU