A Review on Data Analytical Techniques for Predicting HIV Treatment Outcome Using Selected Biomarkers

Analytical techniques for the prediction of treatment outcomes in HIV-infected patients to a state of HIV disease (AIDS) have been examined over more than two decades. Previously, limited knowledge hindered using data analytics techniques to track clinical treatment outcomes. This paper reviews recent studies on HIV treatment prediction over the last five years. A comprehensive search on Google Scholar centered around analytical methods, biomarkers, and their role in predicting the outcomes of HIV treatment. The inclusion criteria were articles published in English between 2019 and March 2024. The search yielded 420 articles limited to the adult population. The evaluation criteria included research design, number of data analytic techniques studied, technique accuracy/efficiency, biomarker assessment methods, impact on data science prediction research, and sample size. A total of 10 studies met the above criteria. These consist of two reviews, three comparative studies, three cross-sectional studies, one cohort design, and one case-control study—the results from various analytic techniques mainly combined with statistical learning, machine learning, and other approaches. However, further evaluation and replication of these novel techniques in more significant, long-term efficacy trials are necessary. Therefore, the objective(s) of this paper review is to discover newer learning algorithms for HIV disease treatment outcomes. A learning algorithm alert or feedback, an innovative concept, may be applied in further research on HIV infection prevention or decreasing the cause of the illness.