# Utilization of Machine Learning Algorithms
## 1 Introduction  
For the past four decades, human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) has been a devastating burden to populations worldwide. According to the recent World Health Organization (WHO) estimates, HIV/AIDS has infected 85.6 million individuals and killed 40.4 million people since the epidemic began [1]. By the end of 2022, at least 39.0 million people were living with HIV (PLHIV), of which 25.6 million lived in Sub-Saharan Africa (SSA) [1]. Despite the global attempts to address HIV/AIDS, high HIV incidence and reluctance to test for HIV persist, particularly in South and Eastern Africa, which bear the heaviest HIV burden [1, 2]. Accelerated action to end the AIDS epidemic by 2030, also known as the Fast-Track (95-95-95), was developed in 2020 bythe Joint United Nations Program on HIV/AIDS (UNAIDS) [3]. This HIV/AIDS response established an interim target for 2025, proposing that 95% of PLHIV know their status, 95% of those who know their status are linked to treatment, and 95% of those who are on treatment are virally suppressed [3]. If achieved, this would mark a magnificent milestone in the history of epidemiology and reshape healthcare sectors for the greater good. As part of the cross-sectoral efforts to meet the UNAIDS 2030 target, the WHO developed a consolidated framework for HIV prevention and care, with a specific emphasis on prioritizing new HIV testing strategies [2]. HIV testing serves as the gateway to realizing the UNAIDS Fast-Track goal because being tested is the only way to be catered for in a specific HIV intervention. However, the recent UNAIDS report indicated that only 86% of PLHIV are aware of their status, while 90% of PLHIV are currently on treatment [4]. Although a significant number of PLHIV have been accurately diagnosed and linked to treatment through conventional counseling and testing approaches over the years, these methods still fall short of adequately serving certain groups [5; 6]. Key populations (KPs), who are often the main drivers of HIV transmission, frequently face stigma and discomfort when accessing public healthcare services, which has led the WHO to recommend HIV self-testing (HIVST) [7]. Furthermore, traditional statistical models, while useful, often struggle to predict HIV risk when applied to complex datasets [6]. As we approach the 2030 agenda, conventional HIV testing methods alone seem inadequate for the ambitious goal of ending HIV as an epidemic [6]. Limitations of traditional HIV testing, including issues related to accessibility, acceptability, and privacy, have become more apparent. As a result, their effectiveness in diagnosing high-risk populations remains questionable [5; 6; 7]. Moreover, the impacts of COVID-19 on the global economy are still fresh, poising resource constraints on the implementation of universal screening for HIV, especially in developing countries [8; 9]. Therefore, these issues, coupled with emerging epidemics, demand the integration of more innovative HIV testing approaches such as machine learning (ML) [10]. Machine learning involves the use of computational and statistical algorithms that learn from complex data to identify hidden patterns and associations, improving the efficacy of predictions and the quality of decisions compared to traditional methods [5; 6; 10]. Recently, experts have developed ML algorithms to discover hidden factors associated with HIV testing [10]. These methods are applied to any human data consisting of socio-behavioral, sociodemographic, and HIV risk information [11; 12; 13]. Machine learning can accurately predict high-HIV-risk groups that could not be identified through traditional approaches [10; 14]. These techniques can enhance early prediction of HIV transmission to develop targeted testing approaches, thereby improving the efficiency of testing services and optimizing the allocation of resources [15; 16]. Interestingly, ML models can also be incorporated into HIV testing devices, ultimately enhancing diagnostic accuracy [17; 18].