Application of Machine Learning in the Classification of HIV Medical Care Status for People Living with HIV in Oshana Region of Namibia.
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Sub-Saharan African Research (IJSSAR)
Abstract
Background: Monitoring of viral load among pregnant and breastfeeding women augments remote
patient management, reduces the risk of mother-to-child transmission of Human Immunodeficiency
Virus (HIV), helps prevent treatment failure and virological rebound.
Objective: This study aimed to develop a machine learning (ML) model that effectively classifies
the medical care status of HIV patients, particularly among pregnant and breastfeeding women, using
integrated historic data of people living with HIV (PLHIV) in Oshana region, Namibia.
Method: A quantitative approach was employed to a cross-sectional dataset of 27,768 patients, from
which 22,347 active patients were selected. Feature selection using a Random Forest classifier was
used to reduce the risk of model overfitting. Three supervised learning models Convolutional Neural
Network (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM were trained using
an 80/20 train-test split. Models were trained under two scenarios: (1) using all 71 demographic and
clinical features and (2) using a reduced set of 5 top feature.
Results: The hybrid CNN-LSTM achieved the highest performance (99.98% accuracy, 98.46%
recall, 99.22% F1-score) and maintained strong results even with fewer features. In contrast, CNN
and LSTM models showed reduced recall, highlighting the hybrid model’s superior ability to
minimize false negatives, critical for identifying high-risk PBFW.
Conclusion: ML models can enhance healthcare decision making by providing accurate predictions
to strengthen continuity of HIV care.
Unique Contribution: This study provides localized evidence on HIV care in Oshana region,
Namibia by applying deep learning to classify the medical care status of pregnant and breastfeeding
women. It demonstrates how routine clinical data can support scalable, data-driven interventions to
improve continuity of care and reduce treatment failure in resource-limited settings.
Key recommendation: Future research should explore alternative hybrid deep learning
architectures, optimize complex hyperparameters, and evaluate diverse feature selection techniques.
Testing on larger datasets is also recommended to assess scalability and generalizability.
Description
Keywords
Pregnant and breastfeeding women, medical care status, HIV care, viral load monitoring
Citation
Nangolo, M., Zodi, G. A. L., Mahalie, R., & Mateus, J. (2025). Application of Machine Learning in the Classification of HIV Medical Care Status for People Living with HIV in Oshana Region of Namibia. International Journal of Sub-Saharan African Research, 3(3), 74-84.https://www.ijssar.com/paper/application-of-machine-learning-in-the-classification-of-hiv-medical-care-status-for-people-living-with-hiv-in-oshana-region