A Hybrid Machine Learning Framework for Early Detection of Hiv-Associated Opportunistic Infections
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The increasing burden of OIs among people living with HIV necessitates accurate and timely predictive systems to support clinical decision-making. The framework leverages clinical, demographic, and treatment-related variables extracted from a real-world dataset obtained from the University of Uyo Teaching Hospital. Data preprocessing techniques, including normalization, feature engineering, and dimensionality reduction were applied to enhance model performance. The proposed model was evaluated using multiple performance metrics, including accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix analysis. Experimental results show that the best-performing configuration achieved an accuracy of 96.79% with strong sensitivity and specificity, demonstrating its effectiveness in distinguishing between infected and non-infected cases. Furthermore, the model demonstrated the use of the ReLU-based activation function. SHAP analysis further enhanced model interpretability by identifying key predictive features such as CD4 count, treatment type, and age. Overall, the results confirm that the proposed hybrid framework is a reliable and clinically meaningful tool for early detection and risk stratification of HIV-associated opportunistic infections.
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Bartl, L., Zeeb, M., Kälin, M., Loosli, T., Notter, J., Furrer, H., and Nemeth, J. (2025). Machine Learning-based Prediction of Active Tuberculosis in People With HIV Using Clinical Data. Clinical Infectious Diseases.
Chen, H., Chen, F., Wang, Y., Cai, E., Pan, W., Li, Y., andSu, F. (2025). A Machine Learning Model for Diagnosing Opportunistic Infections in HIV Patients: Broad Applicability Across Infection Types. Journal of Cellular and Molecular Medicine.
Chen, J., Liu, L., Huang, J., Jiang, Y.-J., Yin, C., Zhang, L., andLu, H. (2024). LSTM-Based Prediction Model for Tuberculosis Among HIV-Infected Patients Using Structured Electronic Medical Records: A Retrospective Machine Learning Study. Journal of Multidisciplinary Healthcare.
Cherezov, D., Dam, T., Najjingo, I., Mbabazi, M., Kisembo, H., Kirenga, B., andMadabhushi, A. (2025). Opportunistic use of artificial intelligence with X-ray imaging for diagnosis of HIV status in tuberculosis patients in Uganda and Tanzania. PLOS Digital Health.
Chew, R., Woods, M., and Paterson, D. L. (2024). Development and validation of supervised machine learning multivariable prediction models for the diagnosis of Pneumocystis jirovecii pneumonia using nasopharyngeal swab PCR in adults in a low-HIV prevalence setting. International Health.
Ekpenyong, M., Etebong, P. I., and Jackson, T. (2019). Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy. Heliyon.
Fu, X., Wu, L., Xun, J., Pütz, B., Zheng, Z., Li, Y., and Müller-Myhsok, B. (2025). Predictive survival modelings for HIV-related cryptococcosis: comparing machine learning approaches. Frontiers in Cellular and Infection Microbiology.
Henderson, H., Napravnik, S., Kosorok, M., Gower, E. W., Kinlaw, A. C., Aiello, A., and van Duin, D. (2022). Predicting Risk of Multidrug-Resistant Enterobacterales Infections Among People With HIV. Open Forum Infectious Diseases.
Inyang, U. G., Eyoh, I. J., Umoh, U. A., Ubong, E. A., Ene, E. E., Obiyo, D. C., andAkponome, B. E. (2025). Optimal deep neural network parameters for power loss minimization analytics. Nigerian Journal of Technology (NIJOTECH), 44(4), pp.1–14. https://doi.org/10.4314/njt.2025.4911
Li, L., Shi, C., Liu, X., Li, W., Luo, Y., Zhang, H., and Shen, L. (2025). A diagnostic model for NTM disease in HIV-positive patients: a machine learning-based analysis with novel inflammatory markers. Frontiers in Immunology.
Mahmud, S., and Hossain, F. (2025). Identification of HIV-Associated Gene Expression Biomarkers Using Machine Learning and Interpretable Artificial Intelligence. bioRxiv.
Ngiam, K., & Khor, I. W. (2019). Big data and machine learning algorithms for health-care delivery. The Lancet Oncology.
Qasem, S. N. (2024). Introducing HeliEns: A Novel Hybrid Ensemble Learning Algorithm for Early Diagnosis of Helicobacter pylori Infection. De Computis.
Sa’adah, L. N., Fauzi, F., Prizka, Arum, R., Haris, M., Yan, andBisoumi, N. (2026). HYBRID RESAMPLING METHOD AND HYPERPARAMETER OPTIMIZATION FOR HIV/AIDS PREDICTION: EVIDENCE FROM EIGHT MACHINE-LEARNING MODELS. JITK (JurnalIlmuPengetahuan Dan TeknologiKomputer).
Sah, A., Elshaikh, R., Shalabi, M., Abbas, A. M., Prabhakar, P. K., Babker, A. M., and Agarwal, S. (2025). Role of Artificial Intelligence and Personalized Medicine in Enhancing HIV Management and Treatment Outcomes. Life.
Scott, I. (2021). Demystifying machine learning: a primer for physicians. Internal Medicine Journal (Print).
Sun, Q., Zhang, K., Xu, Y., Luo, M., Yang, Z., Liu, Q., and Liu, A. (2025). Explainable machine learning for predicting clinical outcomes in HIV/TB co-infection: a comparative retrospective study. BMC Infectious Diseases.
Waring, J., Lindvall, C., andUmeton, R. (2020). Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. Artif. Intell. Medicine.
Xu, J., Huang, Q., Yu, J., Liu, S., Yang, Z., Wang, F., and Xiao, Y. (2022). Metagenomic Next-Generation Sequencing for the Diagnosis of Suspected Opportunistic Infections in People Living with HIV. Infection and Drug Resistance.
Zhou, J., Yang, Y., Xie, Z., Lu, D., Huang, J., Lan, L., and Huang, J. (2023). Dysbiosis of gut microbiota and metabolites during AIDS: implications for CD4+ T cell reduction and immune activation. AIDS (London).
Zhou, Y., Hemmige, V., Dalai, S., Hong, D. K., Muldrew, K., andMohajer, M. (2019). Utility of Whole-Genome Next-Generation Sequencing of Plasma in Identifying Opportunistic Infections in HIV/AIDS. Open AIDS Journal.
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