Human Immunodeficiency Virus (HIV) Diagnosis Using Neuro-Fuzzy Expert System

The world is at a crucial point in its development of effective strategies on the prevention, care and control of HIV/AIDS at the national and provincial levels. Given the necessary resources and expertise, it may be possible to keep the epidemic at bay in most parts of the World, and to considerably reduce the negative impacts of the disease on individuals and society. Early detection of HIV has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose HIV. However many of these tests are extremely complex and subjective and depend heavily on the experience of the clinician. This paper made significant contribution to the ongoing worldwide research on the lasting solution to this enemy of man-HIV. It uses a synergistic combination of neural network (NN) and fuzzy inference systems (Neuro-Fuzzy) to generate a model for the detection of the risk level of patients with HIV. The user friendliness and accuracy rate of HIV diagnosis using neuro-fuzzy system makes its output an interesting one. using neuro-fuzzy system is one of the best ways to deal with the vagueness and imprecision of data in the health care sector, and no doubt will exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better report with reality in medical diagnosis.