ArticleBMC medical research methodology2021
Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa.
Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 4 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Risk and Protective Factors Associated With HIV-Related Mortality in Children Receiving Antiretroviral Therapy: A Model-Based Meta-Regression.JAMA pediatrics · 2026Pooled it
- The use of artificial intelligence based modelling techniques in One Health-related infectious disease studies in Sub-Saharan Africa: a review.Frontiers in artificial intelligence · 2026Pooled it
- Artificial intelligence for HIV care: a global systematic review of current studies and emerging trends.Journal of the International AIDS Society · 2025Pooled it
- Machine learning for personalized risk assessment of HIV, syphilis, gonorrhoea and chlamydia: A systematic review and meta-analysis.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2025Pooled it
- Using machine learning to predict poor adherence to antiretroviral therapy among adolescents with HIV in low resource settings.AIDS (London, England) · 2025Trial
- Self-Reported Prevalence and Predictors of HIV and Gonorrhea Among Primary Healthcare Attendees: A Cross-Sectional Study from Saudi Arabia.Healthcare (Basel, Switzerland) · 2026Article
- Artificial intelligence in HIV research: a structured review and task-oriented clinical framework.Frontiers in digital health · 2026Review
- Integrating artificial intelligence and machine learning in HIV testing interventions in Gauteng Province, South Africa: Opportunities, challenges, and implementation strategies.Southern African journal of HIV medicine · 2026Article
- Machine learning to examine adequate awareness and positive perception of HIV pre-exposure prophylaxis among women in sub-Saharan Africa: evidence from 2021-2024 surveys.BMC infectious diseases · 2025Article
- Large language models and global health equity: a roadmap for equitable adoption in LMICs.The Lancet regional health. Western Pacific · 2025Review
- Development and validation of a predictive model for new HIV infection screening among persons 15 years and above in primary healthcare settings in Kenya: a study protocol.BMJ health & care informatics · 2025Article
- The Application of Machine Learning Algorithms to Predict HIV Testing Using Evidence from the 2002-2017 South African Adult Population-Based Surveys: An HIV Testing Predictive Model.Tropical medicine and infectious disease · 2025Article
- Application of Machine Learning and Emerging Health Technologies in the Uptake of HIV Testing: Bibliometric Analysis of Studies Published From 2000 to 2024.Interactive journal of medical research · 2025Article
- Machine learning to improve HIV screening using routine data in Kenya.Journal of the International AIDS Society · 2025Article
- The Application of Machine Learning Algorithms to Predict HIV Testing in Repeated Adult Population-Based Surveys in South Africa: Protocol for a Multiwave Cross-Sectional Analysis.JMIR research protocols · 2025Article
- The status of machine learning in HIV testing in South Africa: a qualitative inquiry with stakeholders in Gauteng province.Frontiers in digital health · 2025Article
- Protocol: the American Women: Assessing Risk Epidemiologically (AWARE) cohort study.BMC public health · 2024Article
- Explainable artificial intelligence and domain adaptation for predicting HIV infection with graph neural networks.Annals of medicine · 2024Article
- Machine learning prediction of adolescent HIV testing services in Ethiopia.Frontiers in public health · 2024Article
- "But can chatbots understand sex?" Attitudes towards artificial intelligence chatbots amongst sexual and reproductive health professionals: An exploratory mixed-methods study.International journal of STD & AIDS · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimHIV prevention measures in sub-Saharan Africa are still short of attaining the UNAIDS 90-90-90 fast track targets set in 2014. Identifying predictors for HIV status may facilitate targeted screening interventions that improve health care. We aimed at identifying HIV predictors as well as predicting persons at high risk of the infection.
methodWe applied machine learning approaches for building models using population-based HIV Impact Assessment (PHIA) data for 41,939 male and 45,105 female respondents with 30 and 40 variables respectively from four countries in sub-Saharan countries. We trained and validated the algorithms on 80% of the data and tested on the remaining 20% where we rotated around the left-out country. An algorithm with the best mean f1 score was retained and trained on the most predictive variables. We used the model to identify people living with HIV and individuals with a higher likelihood of contracting the disease.
resultsApplication of XGBoost algorithm appeared to significantly improve identification of HIV positivity over the other five algorithms by f1 scoring mean of 90% and 92% for males and females respectively. Amongst the eight most predictor features in both sexes were: age, relationship with family head, the highest level of education, highest grade at that school level, work for payment, avoiding pregnancy, age at the first experience of sex, and wealth quintile. Model performance using these variables increased significantly compared to having all the variables included. We identified five males and 19 females individuals that would require testing to find one HIV positive individual. We also predicted that 4·14% of males and 10.81% of females are at high risk of infection.
conclusionOur findings provide a potential use of the XGBoost algorithm with socio-behavioural-driven data at substantially identifying HIV predictors and predicting individuals at high risk of infection for targeted screening.
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