2021
DOI: 10.1186/s12874-021-01346-2
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Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa

Abstract: Aim HIV 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. Method We applied machine learning approaches for building models using population-based HIV Impact Assessmen… Show more

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Cited by 41 publications
(32 citation statements)
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“…This does not provide a comprehensive and accurate analysis of the data. For example, for HIV [56], about 80 million people have been infected and are alive today. However, the data on their progression to acquired immunodeficiency syndrome (AIDS) and death is not available.…”
Section: Small Size Of Dataset Sample Size or Validation-setmentioning
confidence: 99%
See 1 more Smart Citation
“…This does not provide a comprehensive and accurate analysis of the data. For example, for HIV [56], about 80 million people have been infected and are alive today. However, the data on their progression to acquired immunodeficiency syndrome (AIDS) and death is not available.…”
Section: Small Size Of Dataset Sample Size or Validation-setmentioning
confidence: 99%
“…The traditional modeling methods are not suitable for data with missing values, irregular spacing, and high-dimensional features. The majority of the current ML models are developed and evaluated either on artificially generated data or are experimental [11], [22], [43], [45], [55], [56]. In many cases, these models perform better than existing methods based on evaluation metrics such as accuracy and precision.…”
Section: Lack Of Models To Deal Directly With Real-world Datamentioning
confidence: 99%
“…Machine learning in HIV/AIDS had applied as follows: Machine learning to identify HIV predictors for screening [13]. Machine learning in the prediction of patient-specific current CD 4 cell count to determine the progression of human immunodeficiency.…”
Section: Machine Learning In Health Carementioning
confidence: 99%
“…These methods have previously been used in contexts in which those assumptions are challenged, such as spatial, temporal and spatiotemporal analyses of infectious diseases, e.g. mapping of human leptospirosis [10,11], severe fever with thrombocytopenia syndrome [12], lymphatic filariasis [13], or to identify individuals with a higher risk of HIV infection based on socio-behavioural-driven data [14].…”
Section: Introductionmentioning
confidence: 99%