Early Mortality Risk Prediction in Covid-19 Patients Using an Ensemble of Machine Learning Models

ABSTRACT

COVID-19, which is subsequently named as SARS-CoV-2, First Human case was found in the City of Wuhan, from China, in Dec 2019. After that, the World health organization (WHO) has declared Coronavirus as a Pandemic on 11th March 2020. In this study, our primary aim is to Detect the Severe Covid-19 patient in the Early Stages by looking at the information on admission laboratory values, demographics, comorbidities, admission medications, admission supplementary oxygen orders, discharge, and mortality. 4711 patient's dataset with confirmed SARS-CoV-2 infections are included in the study. Each Patient has total of 85 Features in the Dataset. So, we have Filtered the Top Best 35 features out of 85 features from the Dataset using the seven different feature Selection algorithm and taken the most common features out from the different feature Selection algorithm. After selecting the top most essential features, we have applied around 17 different kinds of ML models like Linear Regression, Logistic regression, SVM, LinearSVC, MLP-Classifier, Decision Tree Classifier, Gradient Boosting Classifier, AdaBoost, Random Forest, XGBoost, LightGBM Classifier, Ridge Classifier, Bagging Classifier, ExtraTreeClassifier, KNN, Naive Bayes, Neural network with Keras, and finally, a Voting Classifier which is the ensemble of all the Top Models from the above-mentioned Models. Finally, all Models are Compared on the basis of Area under the receiver operating characteristic (AUC) get the best AUC as 0.89. © 2021 IEEE.