Predicting HIV Status Using Machine Learning Techniques and Bio-Behavioural Data from the Zimbabwe Population-Based HIV Impact Assessment (ZIMPHIA15-16)-Reference-Cited by-同舟云学术

Predicting HIV Status Using Machine Learning Techniques and Bio-Behavioural Data from the Zimbabwe Population-Based HIV Impact Assessment (ZIMPHIA15-16)

Published:2022 Issue: Volume: Page:247-258
ISSN:2367-3370
Container-title:Artificial Intelligence Trends in Systems
language:
Short-container-title:

Author:

Chingombe Innocent,Musuka Godfrey,Mbunge Elliot,Chemhaka Garikayi,Cuadros Diego F.,Murewanhema Grant,Chaputsira Simbarashe,Batani John,Muchemwa Benhildah,Mapingure Munyaradzi P.,Dzinamarira Tafadzwa

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-09076-9_24

Reference35 articles.

1. Jones, J., Sullivan, P.S., Curran, J.W.: Progress in the HIV epidemic: identifying goals and measuring success. PLoS Med. 16 (2019). https://doi.org/10.1371/JOURNAL.PMED.1002729

2. Global HIV & AIDS statistics—Fact sheet|UNAIDS (n.d.) https://www.unaids.org/en/resources/fact-sheet. Accessed 4 Feb 2022

3. UNAIDS: Global AIDS Update 2018: Miles to Go: Closing Gaps Breaking Barrier Righting Injustices (2019)

4. Carlberg, R., Wolgast, E., Kristensson Hallström, I., Biru, M.: Caregiver and child factors predicting HIV status disclosure among children enrolled on ART: a cross-regional study in Addis Ababa and Oromia, Ethiopia 34, 105–111 (2021). https://doi.org/10.1080/09540121.2021.1918622

5. Doat, A.R., Negarandeh, R., Hasanpour, M.: Disclosure of HIV status to children in sub-Saharan Africa: a systematic review. Medicina 55 (2019). https://doi.org/10.3390/MEDICINA55080433

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Utilization of Machine Learning Algorithms for the Strengthening of HIV Testing: A Systematic Review;Algorithms;2024-08-17

2. Machine Learning Models for Identifying Factors Influencing and Predicting Malaria Among Children Under Five Years in Nigeria;2024 Conference on Information Communications Technology and Society (ICTAS);2024-03-07

3. Towards Data-Driven Artificial Intelligence Models for Monitoring, Modelling and Predicting Illicit Substance Use;Lecture Notes in Networks and Systems;2024

4. Implementation of ensemble machine learning classifiers to predict diarrhoea with SMOTEENN, SMOTE, and SMOTETomek class imbalance approaches;2023 Conference on Information Communications Technology and Society (ICTAS);2023-03

5. Application of machine learning techniques for predicting child mortality and identifying associated risk factors;2023 Conference on Information Communications Technology and Society (ICTAS);2023-03