Proposed Neural Network Model to Estimate Hemorrhagic Fever Iraqi People Statistics

Abstract

Crimean Congo hemorrhagic fever (CCHF) is one of the dangerous diseases like the Ebola and Lassa fever. It was especially sprayed in many countries such as Iraq. In Iraq, many people died by this virus at 2023. This paper was proposed Neural Network (NN) model and it was derived equations for estimating statistic fatality rates for Iraqi people at 2023. The architecture of NN proposed and contained four layers (one input, two hidden, and one output). In addition, the statistic estimated output of fatality rates was evaluated. It concluded that they better than statistic output of fatality rates of traditional statistical method of related work for the same dataset. The Mean Absolute Error (MAE) was found after training and it was near to zero. It was concluded that this proposed NN model is a good performance and more statistically accurate to estimate hemorrhagic fever for fatality rates in Iraq in 2023.