Machine learning approach and geospatial analysis to determine HIV infection, awareness status, and transmission knowledge among adults in Sub-Saharan Africa

Abstract

The machine learning algorithm (decision tree) revealed that the risk of HIV infection is high among individuals who are unaware of their HIV status and lack knowledge about HIV transmission in Southern and eastern parts of Sub-Saharan Africa. The spatial analysis revealed the high-risk areas of HIV infection with low HIV status awareness and HIV transmission knowledge were located in Southern and some Eastern SSA countries. Therefore public health strategies should focus on educating individuals about the importance of knowing their HIV status, transmission knowledge, and ensuring accessible testing options in these affected regions to address the observed spatial disparities in HIV infection, HIV status awareness, and HIV transmission knowledge to achieve the 2030 Sustainable Development Goal of ending the HIV/AIDS epidemic in Africa.