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.

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Depression being a behavioural health disorder is a serious health concern in Zimbabwe and all over the world. If depression goes unaddressed, the consequences are detrimental and have an impact on the way one behaves as an individual and at the societal level. Despite the number of individuals who could benefit from treatment for behavioural health concerns, their difficulties are often unidentified and unaddressed through treatment. Technology carries the unrealised potential to identify people at risk of behavioural health conditions and to inform prevention and intervention strategies.

One of the challenges being faced in the 21st century is child obesity, which is a serious health concern in Zimbabwe and the world. If obesity is not controlled, it has detrimental consequences when a child risks suffering from health challenges such as cancer, type 2 diabetes, heart disease, and osteoarthritis in adulthood. Therefore, it is paramount to have an early prediction of child obesity using the BMI scale. Technology has the unrealized potential to identify people at risk for behavioural health conditions and inform prevention and intervention strategies. In this study, a prediction model was proposed to investigate how technology can be used to predict child obesity. Using a prediction model, this study sought to understand technology's potential value in child obesity. Three different machine learning methods were used to establish accuracy in the prediction model. The findings of this study indicate that it is feasible to use a prediction tool to identify individuals at risk of being diagnosed with obesity, which can facilitate early intervention and improved outcomes.

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