Influenza is regarded as the most common disease in the world. Although some influenza patients recover from fever and other side effects within a week without requiring medical attention, the illness can cause serious sickness or death in others, especially children and the elderly. Information technology now plays an important role  in the development of effective and comprehensive approaches to cope with epidemic diseases in humans. The development of artificial intelligence has greatly expanded the use of neural networks in the epidemiological field. Thus, this study examined the use of artificial neural networks (ANNs) to learn the historical patterns of disease incidence and forecast future occurrence. Experiments using the proposed model showed accurate prediction results. These results promoted the advantages of neural networks for supporting decision makers in developing long term strategies regarding the number of disease incidences. A web-based medical application, based on this architectural prediction model was developed. The model will assist  in the planning and management of suitable strategies to reduce the number of influenza cases.

artificial neural networks, influenza epidemics, prediction model.