Predicting Mortality COVID‑19 Model and Characterize the High Risk of Dying Based on Machine Learning Models - Ali Atiyah, Manal; Ali Kamal, Chra (The Scientific Journal of Cihan University– Sulaimaniya, 2023)
COVID-19 is a respiratory illness that the SARS-CoV-2 virus has caused. It has been declared a pandemic by the (WHO). The surprise increase in infections and the high mortality rates have strained the public healthcare system. Thus, it is fatal to recognize the most remarkable factors for mortality prediction to improve patient treatment strategy. We developed a predictive model using machine learning and AI to identify the health risks and predict the mortality rate of patients with COVID-19. In this study, 4.711 patients with confirmed SARS-CoV-2 infection were included the goal of this study is to develop an AI model that can help medical facilities identify which patients need immediate attention and which ones should be sent to the hospital first. It can also help them, triage patients, during times of overcrowding. various machine learning algorithms used to analyze the mortality rate of patients with COVID-19. These included Random Forests, K-Nearest Neighbors, Neural Networks, Support Vector Machines, and Decision Trees. We applied the feature selection method to identify to improve the machine learning algorithm achievement. the results showed that the Random Forests and Neural Networks approximated the best predictive model with a balanced accuracy of 85%. The model with the best performance supplies a tool to decrease mortality. We split the dataset of COVID-19 patients to analyze our model’s accuracy Finally We used a confusion matrix to analyze the model’s accuracy and other evaluation metrics.