Developing Predictive Model of HIV/AIDS Epidemic Status using machine

Machine learning is dynamic research capable of extracting hidden relationships from these input variables to identify factors that determine the HIV status of clients. This research has attempted to identify determinants of HIV status and predicts the HIV status of the population by analyzing Voluntary Counseling and Testing data pattern using recent data and more variables so as to support the scaling up of knowledge of HIV status. This research work was conducted on the developing of Predictive Models of HIV/AIDS Epidemic Status, a case study of Robe hospital. A major global epidemic, AIDS (acquired immune deficiency syndrome) was initially disease stated in 1981. HIV (human immunodeficiency virus) infection leads to AIDS, which slowly impairs the body's capacity to fight infections and some malignancies by killing or harming immune system cells (T-cells). Although the HIV epidemic has spread widely in Ethiopia, there are notable regional differences in its intensity. I can intended to precisely determine the scale of the HIV/AIDS pandemic in Ethiopia and to investigate how population mobility affected it.