Predicting HIV Status among Men Who Have Sex with Men in Bulawayo & Harare, Zimbabwe Using Bio-Behavioural Data, Recurrent Neural Networks, and Machine Learning Techniques
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Tropical Medicine and Infectious Disease |
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| Online Access: | https://www.mdpi.com/2414-6366/7/9/231 |
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| author | Innocent Chingombe Tafadzwa Dzinamarira Diego Cuadros Munyaradzi Paul Mapingure Elliot Mbunge Simbarashe Chaputsira Roda Madziva Panashe Chiurunge Chesterfield Samba Helena Herrera Grant Murewanhema Owen Mugurungi Godfrey Musuka |
| author_facet | Innocent Chingombe Tafadzwa Dzinamarira Diego Cuadros Munyaradzi Paul Mapingure Elliot Mbunge Simbarashe Chaputsira Roda Madziva Panashe Chiurunge Chesterfield Samba Helena Herrera Grant Murewanhema Owen Mugurungi Godfrey Musuka |
| author_sort | Innocent Chingombe |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T22:19:18Z |
| format | Article |
| id | doaj.art-821734be32594f768b6b4c3243104613 |
| institution | Directory Open Access Journal |
| issn | 2414-6366 |
| language | English |
| last_indexed | 2024-03-09T22:19:18Z |
| publishDate | 2022-09-01 |
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| series | Tropical Medicine and Infectious Disease |
| spelling | doaj.art-821734be32594f768b6b4c32431046132023-11-23T19:18:04ZengMDPI AGTropical Medicine and Infectious Disease2414-63662022-09-017923110.3390/tropicalmed7090231Predicting HIV Status among Men Who Have Sex with Men in Bulawayo & Harare, Zimbabwe Using Bio-Behavioural Data, Recurrent Neural Networks, and Machine Learning TechniquesInnocent Chingombe0Tafadzwa Dzinamarira1Diego Cuadros2Munyaradzi Paul Mapingure3Elliot Mbunge4Simbarashe Chaputsira5Roda Madziva6Panashe Chiurunge7Chesterfield Samba8Helena Herrera9Grant Murewanhema10Owen Mugurungi11Godfrey Musuka12Graduate Business School, Chinhoyi University of Technology, Chinhoyi, ZimbabweICAP, Columbia University, Harare, ZimbabweDepartment of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH 45221, USAICAP, Columbia University, Harare, ZimbabweDepartment of Information Technology, Faculty of Accounting and Informatics, Durban University of Technology, Durban 4000, South AfricaICAP, Columbia University, Harare, ZimbabweSchool of Sociology and Social Policy, University of Nottingham, Nottingham NG7 2RD, UKGraduate Business School, Chinhoyi University of Technology, Chinhoyi, ZimbabweGALZ, Harare, ZimbabweSchool of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth PO1 2UP, UKUnit of Obstetrics and Gynaecology, Department of Primary Health Care Sciences, Faculty of Medicine and Health Sciences, University of Zimbabwe, Harare, ZimbabweMinistry of Health and Child Care, AIDS and TB Programme, Harare, ZimbabweICAP, Columbia University, Harare, ZimbabweHIV 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.https://www.mdpi.com/2414-6366/7/9/231HIV/AIDSstatusMSMdeep learningmachine learningprediction models |
| spellingShingle | Innocent Chingombe Tafadzwa Dzinamarira Diego Cuadros Munyaradzi Paul Mapingure Elliot Mbunge Simbarashe Chaputsira Roda Madziva Panashe Chiurunge Chesterfield Samba Helena Herrera Grant Murewanhema Owen Mugurungi Godfrey Musuka Predicting HIV Status among Men Who Have Sex with Men in Bulawayo & Harare, Zimbabwe Using Bio-Behavioural Data, Recurrent Neural Networks, and Machine Learning Techniques Tropical Medicine and Infectious Disease HIV/AIDS status MSM deep learning machine learning prediction models |
| title | Predicting HIV Status among Men Who Have Sex with Men in Bulawayo & Harare, Zimbabwe Using Bio-Behavioural Data, Recurrent Neural Networks, and Machine Learning Techniques |
| title_full | Predicting HIV Status among Men Who Have Sex with Men in Bulawayo & Harare, Zimbabwe Using Bio-Behavioural Data, Recurrent Neural Networks, and Machine Learning Techniques |
| title_fullStr | Predicting HIV Status among Men Who Have Sex with Men in Bulawayo & Harare, Zimbabwe Using Bio-Behavioural Data, Recurrent Neural Networks, and Machine Learning Techniques |
| title_full_unstemmed | Predicting HIV Status among Men Who Have Sex with Men in Bulawayo & Harare, Zimbabwe Using Bio-Behavioural Data, Recurrent Neural Networks, and Machine Learning Techniques |
| title_short | Predicting HIV Status among Men Who Have Sex with Men in Bulawayo & Harare, Zimbabwe Using Bio-Behavioural Data, Recurrent Neural Networks, and Machine Learning Techniques |
| title_sort | predicting hiv status among men who have sex with men in bulawayo harare zimbabwe using bio behavioural data recurrent neural networks and machine learning techniques |
| topic | HIV/AIDS status MSM deep learning machine learning prediction models |
| url | https://www.mdpi.com/2414-6366/7/9/231 |
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