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p-Index From 2021 - 2026
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All Journal Indonesian Journal of Innovation and Applied Sciences (IJIAS)
Marambi, Sheltar
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mathematics Social Sciences

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Developing a Logistic Regression Machine Learning Model that Predicts Viral Load Outcomes for Children Living with HIV in Gutu District, Zimbabwe Ndlovu, Belinda; Kiwa, Fungai Jacqueline; Muduva, Martin; Chipfumbu, Colletor T.; Marambi, Sheltar
Indonesian Journal of Innovation and Applied Sciences (IJIAS) Vol. 5 No. 3 (2025): October-January
Publisher : CV. Literasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47540/ijias.v5i3.2275

Abstract

HIV remains a major public health issue globally, particularly in poor resource settings such as the Gutu district of Zimbabwe. The study aimed to develop a predictive viral load outcome model for HIV children based on the CRISP-DM research process. Secondary clinical data for children aged 0–17 years in Gutu were retrieved from the Demographic Health Information System (DHIS2). The study identified age, adherence status, gender, and geographical location as correlated with viral load outcomes. A supervised machine learning logistic regression model was trained with data balance and proper management of complexities. Grid search-based hyperparameter tuning was performed to improve model performance further. The evaluation metrics were accuracy, sensitivity, F1 Score, and area under the receiver operating characteristic curve (AUC-ROC). The model’s performance resulted in 89% accuracy, with all the metrics showing a strong performance. A confusion matrix was used to visualize the results. The findings add to the knowledge on viral load outcome prediction and HIV care in Zimbabwe. The findings suggest that early diagnosis and targeted interventions can improve viral load outcomes in children in Gutu. For future research, the development of the model will be based on more representative data sets and applied to other settings to determine differences in other regions and understand the dynamics of HIV care in children.
Developing a graph-based machine learning model for identifying money laundering networks associated with sanctioned entities in a bank in Zimbabwe Ndlovu, Belinda; Kiwa, Fungai Jacqueline; Muduva, Martin; Chipfumbu, Colletor T.; Marambi, Sheltar; Maphosa, Amazing
Indonesian Journal of Innovation and Applied Sciences (IJIAS) Vol. 6 No. 1 (2026): February-May
Publisher : CV. Literasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47540/ijias.v6i1.2306

Abstract

Money laundering networks associated with sanctioned entities pose a significant risk to financial systems, often operating through complex relational transaction structures that evade traditional rule-based monitoring. While graph neural networks have demonstrated promise in financial crime detection, limited work has formally modelled sanction-linked transaction networks within highly imbalanced banking datasets under consistent comparative evaluation. This study proposes a directed weighted graph-based learning framework for identifying sanction-associated money laundering networks using real-world banking transaction data. Transactions were modelled as relational graphs, with accounts as nodes and transfers as weighted edges, and evaluated using a Graph Convolutional Network (GCN) against classical and ensemble classifiers. The proposed model achieved an accuracy of 88.18%, F1-score of 0.7345, ROC-AUC of 0.8968, and a superior Matthews Correlation Coefficient compared to baseline methods. Results demonstrate that relational graph modelling improves the detection of structurally coordinated laundering behaviours that are not captured by independent transaction classifiers. These findings support the integration of graph neural network architectures into anti-money laundering systems to enhance sanction-linked detection capabilities in complex financial networks.
Co-Authors Chipfumbu, Colletor T. Kiwa, Fungai Jacqueline Maphosa, Amazing Muduva, Martin Ndlovu, Belinda