COVID-19 death risk assessment in Iran using artificial neural network

• Main Authors: Ifeoluwapo, R. Adebayo, Supriyanto, Eko, Taheri, Sahar
• Format: Conference or Workshop Item
• Language: English
• Published: 2021
• Subjects: Q Science (General); TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.utm.my/id/eprint/95934/1/EkoSupriyanto2021_COVID19DeathRiskAssessmentinIran.pdf; http://eprints.utm.my/id/eprint/95934/; http://dx.doi.org/10.1088/1742-6596/1964/6/062117

Since the pandemic spread of COVID-19, it has posed a unique public health concern worldwide due to its increased death rate all around the world. The pandemic disease is caused by the SARS-CoV-2, which is the main cause of Middle East Respiratory Syndrome (MERS) and severe acute respiratory syndrome (SARS). Risk assessment is a vital action toward disease risk reduction as it increases the understanding of the risk factors associated with the disease and allows existing data to decide on adequate preventive and mitigation measures. Machine learning techniques have gained strength since 2000, as it has crucial role in data analysis and is really helpful to develop standard mortality models. This study aims to find the best model for data analysis using the Artificial Neural Network (ANN) and other risk factors, which contribute to the high mortality and morbidity associated with COVID-19 in Iran, to predict the risk of death for the people with different situation. A systematic review and meta-analysis were examined by using patient risk factor data from studies done by researchers to estimate COVID-19 death risk. Risk factors for the disease were extracted from an existing study. Using ANN, the best risk prediction for the disease is calculated. Assessment of a different number of hidden neurons with a different training function using the Bayesian Regularization algorithm, the best training function for the ANN model with 5 hidden neurons is found to have the most satisfying results. The coefficient of determination (R) and Root Mean Square Error (RMSE) was 9.99999e-1 and 4.54201e-19 respectively.