Towards Data-Driven Artificial Intelligence Models for Monitoring, Modelling and Predicting Illicit Substance Use

Mbunge, Elliot; Batani, John; Chitungo, Itai; Moyo, Enos; Musuka, Godfrey; Muchemwa, Benhildah; Dzinamarira, Tafadzwa

doi:10.1007/978-3-031-54820-8_29

Cited by 1 publication

(1 citation statement)

References 54 publications

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“…Among other population groups, men who have sex with men (MSM) are at high risk of sexually transmitted infections and human immunodeficiency virus (HIV) globally [ 1 ], accounting for 23% of new adult HIV infections in 2019[ 2 ]. Men who have sex with men are more vulnerable to HIV infection than the general population because of their behavioural and biological factors, including Unprotected sexual practices, unprotected anal intercourse[ 3 ], multiple sex partners, and substance use [ 4 , 5 ]. These factors, along with criminalization, stigmatisation[ 6 ], fear of discrimination [ 7 ], poor access to care and lack of early STI screening tools in some countries make MSM more vulnerable and increase the risk of HIV infection [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting sexually transmitted infections among men who have sex with men in Zimbabwe using deep learning and ensemble machine learning models

Mugurungi,

Mbunge,

Birri-Makota

et al. 2024

PLOS Digit Health

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There is a substantial increase in sexually transmitted infections (STIs) among men who have sex with men (MSM) globally. Unprotected sexual practices, multiple sex partners, criminalization, stigmatisation, fear of discrimination, substance use, poor access to care, and lack of early STI screening tools are among the contributing factors. Therefore, this study applied multilayer perceptron (MLP), extremely randomized trees (ExtraTrees) and XGBoost machine learning models to predict STIs among MSM using bio-behavioural survey (BBS) data in Zimbabwe. Data were collected from 1538 MSM in Zimbabwe. The dataset was split into training and testing sets using the ratio of 80% and 20%, respectively. The synthetic minority oversampling technique (SMOTE) was applied to address class imbalance. Using a stepwise logistic regression model, the study revealed several predictors of STIs among MSM such as age, cohabitation with sex partners, education status and employment status. The results show that MLP performed better than STI predictive models (XGBoost and ExtraTrees) and achieved accuracy of 87.54%, recall of 97.29%, precision of 89.64%, F1-Score of 93.31% and AUC of 66.78%. XGBoost also achieved an accuracy of 86.51%, recall of 96.51%, precision of 89.25%, F1-Score of 92.74% and AUC of 54.83%. ExtraTrees recorded an accuracy of 85.47%, recall of 95.35%, precision of 89.13%, F1-Score of 92.13% and AUC of 60.21%. These models can be effectively used to identify highly at-risk MSM, for STI surveillance and to further develop STI infection screening tools to improve health outcomes of MSM.

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