A Hybrid Machine Learning Framework for Early Detection of Hiv-Associated Opportunistic Infections

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.

HIV, Infection, Machine Learning, Opportunistic Infections, DNN

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