Abstract:
HIV and AIDS continue to be major public health concerns globally. Despite significant
progress in addressing their impact on the general population and achieving epidemic control, there
is a need to improve HIV testing, particularly among men who have sex with men (MSM). This
study applied deep and machine learning algorithms such as recurrent neural networks (RNNs), the
bagging classifier, gradient boosting classifier, support vector machines, and Naïve Bayes classifier
to predict HIV status among MSM using the dataset from the Zimbabwe Ministry of Health and
Child Care. RNNs performed better than the bagging classifier, gradient boosting classifier, support
vector machines, and Gaussian Naïve Bayes classifier in predicting HIV status. RNNs recorded a
high prediction accuracy of 0.98 as compared to the Gaussian Naïve Bayes classifier (0.84), bagging
classifier (0.91), support vector machine (0.91), and gradient boosting classifier (0.91). In addition,
RNNs achieved a high precision of 0.98 for predicting both HIV-positive and -negative cases, a recall
of 1.00 for HIV-negative cases and 0.94 for HIV-positive cases, and an F1-score of 0.99 for HIV-negative
cases and 0.96 for positive cases. HIV status prediction models can significantly improve early HIV
screening and assist healthcare professionals in effectively providing healthcare services to the MSM
community. The results show that integrating HIV status prediction models into clinical software
systems can complement indicator condition-guided HIV testing strategies and identify individuals
that may require healthcare services, particularly for hard-to-reach vulnerable populations like MSM.
Future studies are necessary to optimize machine learning models further to integrate them into
primary care. The significance of this manuscript is that it presents results from a study population
where very little information is available in Zimbabwe due to the criminalization of MSM activities in
the country. For this reason, MSM tends to be a hidden sector of the population, frequently harassed and arrested. In almost all communities in Zimbabwe, MSM issues have remained taboo, and stigma
exists in all sectors of society.
