Evaluating Deep Learning Models for HIV/AIDS Classification: A Comparative Study Using Clinical and Laboratory Data
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Gregorius Airlangga
*
Atma Jaya Catholic University of Indonesia, Jakarta,
Indonesia
(*) Corresponding Author
Abstract
The accurate classification of HIV/AIDS status is critical for effective diagnosis, treatment planning, and disease management. This study evaluates the performance of four deep learning models: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) on a comprehensive clinical and laboratory dataset derived from the AIDS Clinical Trials Group Study 175. The dataset includes features such as demographic information, treatment history, and immune markers like CD4 and CD8 counts. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied, followed by stratified 10-fold cross-validation to ensure robust evaluation. Each model's performance was assessed using metrics including accuracy, precision, recall, F1-score, and ROC-AUC. GRU emerged as the most effective model, achieving the highest accuracy (71.04%) and ROC-AUC (57.72%), demonstrating its robustness in handling sequential data. CNN and LSTM showed competitive performance, particularly in balancing precision and recall. However, all models faced challenges in recall, highlighting difficulties in identifying minority-class samples. The findings underscore the potential of GRU for HIV/AIDS classification while identifying limitations in current approaches to handling class imbalance. Future work will explore advanced architectures, such as attention mechanisms and hybrid models, to further improve sensitivity and robustness. This study contributes to the growing body of research on applying deep learning to healthcare, with implications for improving diagnostic accuracy and patient outcomes.
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References
R. B. Sonawane and G. D. Barkade, “Literature Review on Acquired Immunodeficiency Syndrome (AIDS).,” Syst. Rev. Pharm., vol. 14, no. 5, 2023.
E. Kokori et al., “Implications of long-acting antiretrovirals (LAARVs) for HIV treatment in Sub-Saharan Africa,” Discov. Public Heal., vol. 21, no. 1, pp. 1–7, 2024.
J. Yang, X. Zheng, S. Zhang, and H. Wang, “Epidemiology-based Analysis of Characteristics of Dual Infection of Tuberculosis/Acquired Immune Deficiency Syndrome (AIDS) and Drug Resistance Mechanism of Related Genes,” Cell. Mol. Biol., vol. 68, no. 2, pp. 109–118, 2022.
Jocelyn et al., “HIV/AIDS in Indonesia: current treatment landscape, future therapeutic horizons, and herbal approaches,” Front. Public Heal., vol. 12, p. 1298297, 2024.
E. Kumah, D. S. Boakye, R. Boateng, and E. Agyei, “Advancing the global fight against HIV/Aids: Strategies, barriers, and the road to eradication,” Ann. Glob. Heal., vol. 89, no. 1, 2023.
F. E. T. Foka and H. T. Mufhandu, “Current ARTs, virologic failure, and implications for AIDS management: a systematic review,” Viruses, vol. 15, no. 8, p. 1732, 2023.
S. Qiao, X. Li, B. Olatosi, and S. D. Young, “Utilizing Big Data analytics and electronic health record data in HIV prevention, treatment, and care research: a literature review,” AIDS Care, vol. 36, no. 5, pp. 583–603, 2024.
Y. Li et al., “The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis,” BMC Infect. Dis., vol. 24, no. 1, p. 474, 2024.
R. Saha, L. Malviya, A. Jadhav, and R. Dangi, “Early stage HIV diagnosis using optimized ensemble learning technique,” Biomed. Signal Process. Control, vol. 89, p. 105787, 2024.
A. Rehman, S. Naz, and I. Razzak, “Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities,” Multimed. Syst., vol. 28, no. 4, pp. 1339–1371, 2022.
S. Asif et al., “Advancements and Prospects of Machine Learning in Medical Diagnostics: Unveiling the Future of Diagnostic Precision,” Arch. Comput. Methods Eng., pp. 1–31, 2024.
A. Zhang, L. Xing, J. Zou, and J. C. Wu, “Shifting machine learning for healthcare from development to deployment and from models to data,” Nat. Biomed. Eng., vol. 6, no. 12, pp. 1330–1345, 2022.
A. Velu, “AIDS Virus Infection Prediction.” Kaggle, 2023.
S. N. Mohsin et al., “The role of artificial intelligence in prediction, risk stratification, and personalized treatment planning for congenital heart diseases,” Cureus, vol. 15, no. 8, 2023.
M. Athar, “Potentials of artificial intelligence in familial hypercholesterolemia: Advances in screening, diagnosis, and risk stratification for early intervention and treatment,” Int. J. Cardiol., vol. 412, p. 132315, 2024.
R. Guido, S. Ferrisi, D. Lofaro, and D. Conforti, “An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review,” Information, vol. 15, no. 4, p. 235, 2024.
E. S. Mohamed, T. A. Naqishbandi, S. A. C. Bukhari, I. Rauf, V. Sawrikar, and A. Hussain, “A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms,” Healthc. Anal., vol. 3, p. 100185, 2023.
A. Makandar and M. N. Jadhav, “Disease Recognition in Medical Images Using CNN-LSTM-GRU Ensemble, a Hybrid Deep Learning,” in 2023 7th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), 2023, pp. 1–9.
I. D. Mienye, T. G. Swart, and G. Obaido, “Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications,” Information, vol. 15, no. 9, p. 517, 2024.
Z. Tarek et al., “An optimized model based on deep learning and gated recurrent unit for COVID-19 death prediction,” Biomimetics, vol. 8, no. 7, p. 552, 2023.
M. A. Basri, “Evaluating the Usefulness of Synthetic Data in Healthcare: Applications in Predictive Modeling and Privacy Protection,” University of Waterloo, 2024.
S. N. Manivannan, C. D. Arenas, N. D. Grubaugh, and C. B. Ogbunugafor, “The importance of epistasis in the evolution of viral pathogens,” Evolution (N. Y)., vol. 20, p. 23.
E. G. Anderson, D. R. Keith, and J. Lopez, “Opportunities for system dynamics research in operations management for public policy,” Prod. Oper. Manag., vol. 32, no. 6, pp. 1895–1920, 2023.
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