Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea)
BackgroundViral load (VL) suppression is key to ending the global HIV epidemic, and predicting it is critical for healthcare providers and people living with HIV (PLHIV). Traditional research has focused on statistical analysis, but machine learning (ML) is gradually influencing HIV clinical care. W...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1446876/full |
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| author | Degninou Yehadji Degninou Yehadji Geraldine Gray Carlos Arias Vicente Petros Isaakidis Petros Isaakidis Abdourahimi Diallo Saa Andre Kamano Thierno Saidou Diallo |
| author_facet | Degninou Yehadji Degninou Yehadji Geraldine Gray Carlos Arias Vicente Petros Isaakidis Petros Isaakidis Abdourahimi Diallo Saa Andre Kamano Thierno Saidou Diallo |
| author_sort | Degninou Yehadji |
| collection | DOAJ |
| description | BackgroundViral load (VL) suppression is key to ending the global HIV epidemic, and predicting it is critical for healthcare providers and people living with HIV (PLHIV). Traditional research has focused on statistical analysis, but machine learning (ML) is gradually influencing HIV clinical care. While ML has been used in various settings, there’s a lack of research supporting antiretroviral therapy (ART) programs, especially in resource-limited settings like Guinea. This study aims to identify the most predictive variables of VL suppression and develop ML models for PLHIV in Conakry (Guinea).MethodsAnonymized data from HIV patients in eight Conakry health facilities were pre-processed, including variable recoding, record removal, missing value imputation, grouping small categories, creating dummy variables, and oversampling the smallest target class. Support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), random forest (RF), and four stacked models were developed. Optimal parameters were determined through two cross-validation loops using a grid search approach. Sensitivity, specificity, predictive positive value (PPV), predictive negative value (PNV), F-score, and area under the curve (AUC) were computed on unseen data to assess model performance. RF was used to determine the most predictive variables.ResultsRF (94% F-score, 82% AUC) and NB (89% F-score, 82% AUC) were the most optimal models to detect VL suppression and non-suppression when applied to unseen data. The optimal parameters for RF were 1,000 estimators and no maximum depth (Random state = 40), and it identified Regimen schedule_6-Month, Duration on ART (months), Last ART CD4, Regimen schedule_Regular, and Last Pre-ART CD4 as top predictors for VL suppression.ConclusionThis study demonstrated the capability to predict VL suppression but has some limitations. The results are dependent on the quality of the data and are specific to the Guinea context and thus, there may be limitations with generalizability. Future studies may be to conduct a similar study in a different context and develop the most optimal model into an application that can be tested in a clinical context. |
| format | Article |
| id | doaj-art-bc7fa19ebeb74cfa83cc6181454e095c |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-bc7fa19ebeb74cfa83cc6181454e095c2025-08-20T03:42:39ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-03-01810.3389/frai.2025.14468761446876Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea)Degninou Yehadji0Degninou Yehadji1Geraldine Gray2Carlos Arias Vicente3Petros Isaakidis4Petros Isaakidis5Abdourahimi Diallo6Saa Andre Kamano7Thierno Saidou Diallo8Médecins Sans Frontières Belgique, Guinea Mission, Conakry, GuineaTechnological University Dublin, School of Informatics and Cybersecurity, Dublin, IrelandTechnological University Dublin, School of Informatics and Cybersecurity, Dublin, IrelandMédecins Sans Frontières Belgique, Guinea Mission, Conakry, GuineaMédecins Sans Frontières, Southern Africa Medical Unit, Cape Town, South AfricaUniversity of Ioannina School of Medicine, Department of Hygiene and Epidemiology, Clinical and Molecular Epidemiology Unit, Ioannina, GreeceMédecins Sans Frontières Belgique, Guinea Mission, Conakry, GuineaMédecins Sans Frontières Belgique, Guinea Mission, Conakry, GuineaMinistry of Health and Public Hygiene, National HIV and Hepatitis Control Program (PNLSH), Conakry, GuineaBackgroundViral load (VL) suppression is key to ending the global HIV epidemic, and predicting it is critical for healthcare providers and people living with HIV (PLHIV). Traditional research has focused on statistical analysis, but machine learning (ML) is gradually influencing HIV clinical care. While ML has been used in various settings, there’s a lack of research supporting antiretroviral therapy (ART) programs, especially in resource-limited settings like Guinea. This study aims to identify the most predictive variables of VL suppression and develop ML models for PLHIV in Conakry (Guinea).MethodsAnonymized data from HIV patients in eight Conakry health facilities were pre-processed, including variable recoding, record removal, missing value imputation, grouping small categories, creating dummy variables, and oversampling the smallest target class. Support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), random forest (RF), and four stacked models were developed. Optimal parameters were determined through two cross-validation loops using a grid search approach. Sensitivity, specificity, predictive positive value (PPV), predictive negative value (PNV), F-score, and area under the curve (AUC) were computed on unseen data to assess model performance. RF was used to determine the most predictive variables.ResultsRF (94% F-score, 82% AUC) and NB (89% F-score, 82% AUC) were the most optimal models to detect VL suppression and non-suppression when applied to unseen data. The optimal parameters for RF were 1,000 estimators and no maximum depth (Random state = 40), and it identified Regimen schedule_6-Month, Duration on ART (months), Last ART CD4, Regimen schedule_Regular, and Last Pre-ART CD4 as top predictors for VL suppression.ConclusionThis study demonstrated the capability to predict VL suppression but has some limitations. The results are dependent on the quality of the data and are specific to the Guinea context and thus, there may be limitations with generalizability. Future studies may be to conduct a similar study in a different context and develop the most optimal model into an application that can be tested in a clinical context.https://www.frontiersin.org/articles/10.3389/frai.2025.1446876/fullHIVantiretroviral therapyviral loadmachine learningpredictionclassification |
| spellingShingle | Degninou Yehadji Degninou Yehadji Geraldine Gray Carlos Arias Vicente Petros Isaakidis Petros Isaakidis Abdourahimi Diallo Saa Andre Kamano Thierno Saidou Diallo Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea) Frontiers in Artificial Intelligence HIV antiretroviral therapy viral load machine learning prediction classification |
| title | Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea) |
| title_full | Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea) |
| title_fullStr | Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea) |
| title_full_unstemmed | Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea) |
| title_short | Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea) |
| title_sort | development of machine learning algorithms to predict viral load suppression among hiv patients in conakry guinea |
| topic | HIV antiretroviral therapy viral load machine learning prediction classification |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1446876/full |
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