Application of Fuzzy Theory in Risk Assessment of Human Immunodeficiency Virus Infection

This study aims to explore the feasibility and effectiveness of applying Fuzzy Theory to the risk assessment of Human Immunodeficiency Virus (HIV) infection. In recent years, the global number of infections has rapidly expanded, with many patients unaware of their own infection status, leading to potential carriers around them. Due to the complexity and diversity of HIV infection, traditional risk assessment methods often fall short. Therefore, Fuzzy Theory is introduced as a mathematical tool to address this issue. This research uses data from a case study in a tertiary hospital in Henan Province, published in a journal on dermatology and venereology in mainland China. Firstly, variables related to HIV infection are analyzed, including Viral Load, Cluster of Differentiation 4 (CD4+) cell concentration, and protein 24 (P24) antigen, which are important indicators for measuring the extent and risk of HIV infection. Then, a risk assessment model for HIV infection is developed using Fuzzy Theory, and the risk of HIV infection is evaluated through fuzzy logic operations. The results show that the assessment model based on Fuzzy Theory can effectively quantify and assess the risk of HIV infection in different individuals, providing scientific evidence for the prevention and control of HIV epidemics. It also assists physicians in quickly understanding patients’ conditions, thereby reducing the risk of HIV transmission.