| dc.description.abstract |
HIV continues to be a global health concern that necessitates cutting-edge methods of diagnosis and treatment. Owing to the intricate nature of the HIV pandemic, specific strategies are needed to pinpoint vulnerable people. This study tackles the challenge of precise identification within specific HIV target groups, namely Adolescent Girls and Young Women (AGYW), High-Risk Men (HRM), and Female Sex Workers (FSW). Leveraging machine learning algorithms include Support vector machine, XGBoost, Random forest and linear regression. The research integrates locally sourced datasets from hospital records, aiming to elevate intervention precision. The study seeks to transform public health by introducing a data-driven approach to unravel intricate relationships and variables influencing HIV prevalence among distinct target groups. Despite progress in global health efforts, traditional methods grapple with precision and efficiency limitations. The adoption of machine learning offers a promising solution, contributing to a nuanced understanding of dynamics within key populations. Addressing gaps in existing literature particularly the scarcity of studies at the intersection of machine learning and the identification of specific HIV target groups using locally collected datasets. The study rigorously evaluates the performance of four algorithms on an HIV service delivery dataset. Results indicate consistently high accuracy across all models, with ensemble approaches (XGBoost and Random Forest) slightly outperforming others. Notably, Support Vector Machine achieved 96.33% accuracy, XGBoost reached 96.51%, Random Forest attained 96.49%, and Linear Regression demonstrated commendable accuracy at 96.28%. This research significantly contributes to advancing machine learning applications in healthcare and addresses a crucial gap in the current body of knowledge. |
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