The worldwide pandemic Coronavirus (Covid-19) is a new viral disease that spreads mostly through nasal discharge and saliva from the lips while coughing or sneezing. This highly infectious disease spreads quickly and can overwhelm healthcare systems if not controlled. However, the employment of machine learning algorithms to monitor analytical data has a substantial influence on the speed of decision-making in some government entities. ML algorithms trained on labeled patients’ symptoms cannot discriminate between diverse types of diseases such as COVID-19. Cough, fever, headache, sore throat, and shortness of breath were common symptoms of many bacterial and viral diseases.
This research focused on the numerous tendencies and projected expansion of the Iraq pandemic to encourage people and governments to take preventive measures. This work is an established basic benchmark for demonstrating machine learning's capabilities for pandemic prediction.
The suggested approach for forecasting the number of COVID-19 cases can assist governments in taking safeguards to avoid the disease's spread. We have demonstrated the effectiveness of our strategy using publicly available datasets and models. A polynomial network is trained on this premise, and the parameters are optimized using frequent weighting. When compared to linear models, the polynomial model predicts better and is more effective in forecasting COVID-19 new confirmed cases. As well, it aims to analyze the spread of COVID-19 in Iraq and optimize polynomial regression. In time series-based models, curve fitting using frequent weighting to implement models such as linear regression and polynomial regression is utilized to estimate the new daily infection number. The datasets were collected from March 13, 2020, to December 12, 2021. The continuous COVID-19 pandemic puts both human lives and the economy at risk. If AI could forecast the next daily hospitalization number, it may be a useful tool in combating this pandemic sickness.
