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

ABSTRACT:

Background

HIV/AIDS remains a major public health challenge, in Sub-Saharan Africa (SSA). In 2020, 16% of people living with HIV did not know their HIV status in SSA. Understanding the geospatial distribution of HIV infection, awareness status, and transmission knowledge is crucial for designing effective prevention and control strategies to end the HIV/AIDS pandemic by 2030. However, to the best of our literature searching the evidence of geospatial analysis and a machine learning algorithm, specifically a decision tree to decide on a Sustainability Development Goal (SDG), and to establish a clear pathway of HIV awareness status and HIV infection rates in each region of SSA is limited. Therefore, this study aims to determine HIV Infection, awareness status, and transmission knowle

SUBMITTER: Endawkie A 

PROVIDER: S-EPMC11667932 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature