A Data Driven Approach for Automated Risk Assessment and Survival Prediction of Coronavirus Using Artificial Neural Networks
Coronavirus is proved to be a severe epidemic diseasethroughout the world. Despite of endeavoring lot of medicalfacilities for mitigating with this pandemic, still the number ofinfected cases increased rapidly, which leads to lack of healthcareresources (i.e. hospitals, doctors and other healthcare amenities). Early stage risk prediction by analyzing several clinical andbehavioral risk factors is considered to be a promising solutionfor prescribing appropriate triage to patients and to reduce themortality rate due to this fetal disease. To cope up with thisproblem, in our study we have proposed a deep learning basedapproach for the early stage prediction of risk of infection andrisk of mortality in individuals possessing certain risk factors. Wehave utilized a publically available covid-19 dataset incorporatingseveral risk factors that may cause this infection. For the selectionof most significant risk factors i.e. with respect to their level ofimportance in risk prediction, we have employed three featuresselection techniques (i.e. f_classif, PCA and Tree). The set ofextracted features are the utilized for the training of proposed ANN for the prediction of infection risk and mortality risk due tocovid-19. For the performance analysis of proposed method, fourdifferent evaluation metrics are being employed including: MSE,MAE, ME and EV. The proposed model has achieved a minimum loss (MSE) of 0.00137 for infection risk prediction and MSE of0.000012 for mortality risk prediction.
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This is an open Access Article published by Research Center of Computing & Biomedical Informatics (RCBI), Lahore, Pakistan under CCBY 4.0 International License