Early Detection of HIV Infection with Machine Learning from Blood Test Results

In this work, machine learning and blood biomarkers are combined for the first time to diagnose HIV early. A descriptive study methodology including secondary data collecting was used in conjunction with a deductive technique that drew on an interpretivist mindset. The analysis uncovered unique biomarker patterns, with viral load and CD4 cell numbers exhibiting critical importance. The most accurate algorithm was the Support Vector Machine (92.5%), which was closely surpassed by Random Forest (90.3%). The critical necessity of particular biomarkers in proper categorization has been highlighted by the feature importance. The model's robustness and generalizability were confirmed by validation on a different dataset. This strategy has the potential to transform HIV screening and provide a vital tool in the fight against the worldwide epidemic. Future studies must focus on improving the model using various datasets, investigating improved biomarker technologies, and carrying out application studies in the actual world.