{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T00:22:09Z","timestamp":1792196529671,"version":"4.4.0"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Continuously growing of HIV incidence among men who have sex with men (MSM), as well as the low rate of HIV testing of MSM in China, demonstrates a need for innovative strategies to improve the implementation of HIV prevention. The use of machine learning algorithms is an increasing tendency in disease diagnosis prediction. We aimed to develop and validate machine learning models in predicting HIV infection among MSM that can identify individuals at increased risk of HIV acquisition for transmission-reduction interventions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We extracted data from MSM sentinel surveillance in Zhejiang province from 2018 to 2020. Univariate logistic regression was used to select significant variables in 2018\u20132019 data (\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.05). After data processing and feature selection, we divided the model development data into two groups by stratified random sampling: training data (70%) and testing data (30%). The Synthetic Minority Oversampling Technique (SMOTE) was applied to solve the problem of unbalanced data. The evaluation metrics of model performance were comprised of accuracy, precision, recall, F-measure, and the area under the receiver operating characteristic curve (AUC). Then, we explored three commonly-used machine learning algorithms to compare with logistic regression (LR), including decision tree (DT), support vector machines (SVM), and random forest (RF). Finally, the four models were validated prospectively with 2020 data from Zhejiang province.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 6,346 MSM were included in model development data, 372 of whom were diagnosed with HIV. In feature selection, 12 variables were selected as model predicting indicators. Compared with LR, the algorithms of DT, SVM, and RF improved the classification prediction performance in SMOTE-processed data, with the AUC of 0.778, 0.856, 0.887, and 0.942, respectively. RF was the best-performing algorithm (accuracy = 0.871, precision = 0.960, recall = 0.775, F-measure = 0.858, and AUC = 0.942). And the RF model still performed well on prospective validation (AUC = 0.846).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Machine learning models are substantially better than conventional LR model and RF should be considered in prediction tools of HIV infection in Chinese MSM. Further studies are needed to optimize and promote these algorithms and evaluate their impact on HIV prevention of MSM.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2022.967681","type":"journal-article","created":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T02:52:35Z","timestamp":1661395955000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":45,"title":["Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation"],"prefix":"10.3389","volume":"10","author":[{"given":"Jiajin","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhua","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siqing","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiezhe","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengliang","family":"Chai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,8,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1186\/s12955-018-1020-x","article-title":"Identifying factors associated with depression among men living with HIV\/AIDS and undergoing antiretroviral therapy: a cross-sectional study in Heilongjiang, China","volume":"16","author":"Liu","year":"2018","journal-title":"Health Qual Life Outcomes."},{"key":"B2","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1590\/1516-3180.2017.0130100917","article-title":"Epidemiological situation of acquired immunodeficiency syndrome (AIDS)-related mortality in a municipality in northeastern Brazil. A retrospective cross-sectional study","volume":"136","author":"Silva","year":"2018","journal-title":"S\u00e3o Paulo Med J."},{"key":"B3","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/s10461-017-1840-4","article-title":"HIV Incidence and Care Linkage among MSM First-Time-Testers in Shenyang, China 2012-2014","volume":"22","author":"Zhang","year":"2018","journal-title":"AIDS Behav."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1186\/s12879-019-4559-1","article-title":"The prevalence of HIV among MSM in China: a large-scale systematic analysis","volume":"19","author":"Dong","year":"2019","journal-title":"BMC Infect Dis."},{"key":"B5","doi-asserted-by":"publisher","first-page":"2215","DOI":"10.1097\/00002030-200310170-00010","article-title":"Methods and procedures for estimating HIV\/AIDS and its impact: the UNAIDS\/WHO estimates for the end of 2001","volume":"17","author":"Walker","year":"2003","journal-title":"AIDS."},{"key":"B6","doi-asserted-by":"publisher","first-page":"111","DOI":"10.13419\/j.cnki.aids.2018.02.01","article-title":"Update on the AIDS\/STD epidemic in China in December 2017","volume":"24","year":"2018","journal-title":"Chin J AIDS STD"},{"key":"B7","doi-asserted-by":"publisher","first-page":"64","DOI":"10.13419\/j.cnki.aids.2012.02.007","article-title":"Update on the AIDS\/STD epidemic in China in 2011","volume":"18","year":"2012","journal-title":"Chin J AIDS STD"},{"key":"B8","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1016\/j.jana.2018.05.003","article-title":"Testing and associated factors among men who have sex with men in Changsha, China","volume":"29","author":"Zhou","year":"2018","journal-title":"J Assoc Nurses AIDS Care."},{"key":"B9","doi-asserted-by":"publisher","first-page":"e251","DOI":"10.2196\/jmir.7251","article-title":"Social media engagement and HIV testing among men who have sex with men in China: a nationwide cross-sectional survey","volume":"19","author":"Cao","year":"2017","journal-title":"J Med Internet Res."},{"key":"B10","doi-asserted-by":"publisher","first-page":"21133","DOI":"10.7448\/IAS.19.1.21133","article-title":"The UNAIDS 90-90-90 target: a clear choice for ending AIDS and for sustainable health and development","volume":"19","author":"Sidib\u00e9","year":"2016","journal-title":"J Int AIDS Soc."},{"key":"B11","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1097\/OLQ.0b013e3181a9cc41","article-title":"Prediction of HIV acquisition among men who have sex with men","volume":"36","author":"Menza","year":"2009","journal-title":"Sex Transm Dis."},{"key":"B12","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1093\/cid\/civ335","article-title":"Development and validation of the San Diego Early Test Score to predict acute and early HIV infection risk in men who have sex with men","volume":"61","author":"Hoenigl","year":"2015","journal-title":"Clin Infect Dis."},{"key":"B13","doi-asserted-by":"publisher","first-page":"2267","DOI":"10.1007\/s10461-018-2129-y","article-title":"Risk prediction score for HIV infection: development and internal validation with cross-sectional data from men who have sex with men in China","volume":"22","author":"Yin","year":"2018","journal-title":"AIDS Behav."},{"key":"B14","doi-asserted-by":"publisher","first-page":"6873891","DOI":"10.1155\/2020\/6873891","article-title":"Identification of potential type II diabetes in a large-scale Chinese population using a systematic machine learning framework","volume":"2020","author":"Xue","year":"2020","journal-title":"J Diabetes Res."},{"key":"B15","doi-asserted-by":"publisher","first-page":"6696041","DOI":"10.1155\/2021\/6696041","article-title":"A noninvasive prediction model for hepatitis B virus disease in patients with HIV: based on the population of Jiangsu, China","volume":"2021","author":"Yin","year":"2021","journal-title":"Biomed Res Int."},{"key":"B16","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine learning in medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation."},{"key":"B17","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1038\/s41574-021-00543-9","article-title":"Machine intelligence in non-invasive endocrine cancer diagnostics","volume":"18","author":"Thomasian","year":"2022","journal-title":"Nat Rev Endocrinol."},{"key":"B18","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J Artif Intell Res."},{"key":"B19","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1515\/med-2021-0282","article-title":"Breast cancer recurrence prediction with ensemble methods and cost-sensitive learning","volume":"16","author":"Yang","year":"2021","journal-title":"Open Med."},{"key":"B20","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1016\/S0895-4356(01)00372-9","article-title":"Logistic regression in the medical literature: standards for use and reporting, with particular attention to one medical domain","volume":"54","author":"Bagley","year":"2001","journal-title":"J Clin Epidemiol."},{"key":"B21","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1186\/s12911-020-1060-0","article-title":"A decision tree to improve identification of pathogenic mutations in clinical practice","volume":"20","author":"Nascimento","year":"2020","journal-title":"BMC Med Inform Decis Mak."},{"key":"B22","doi-asserted-by":"publisher","first-page":"41","DOI":"10.21873\/cgp.20063","article-title":"Applications of support vector machine (SVM) learning in cancer genomics","volume":"15","author":"Huang","year":"2018","journal-title":"Cancer Genomics Proteomics."},{"key":"B23","doi-asserted-by":"publisher","first-page":"S75","DOI":"10.1097\/QAD.0000000000002867","article-title":"Adolescent HIV-related behavioural prediction using machine learning: a foundation for precision HIV prevention","volume":"35","author":"Wang","year":"2021","journal-title":"AIDS."},{"key":"B24","doi-asserted-by":"publisher","first-page":"930","DOI":"10.1111\/cbdd.13819","article-title":"Evaluation of the performance of various machine learning methods on the discrimination of the active compounds","volume":"97","author":"Shamsara","year":"2021","journal-title":"Chem Biol Drug Des."},{"key":"B25","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1093\/ije\/dyz274","article-title":"Reflection on modern methods: revisiting the area under the ROC Curve","volume":"49","author":"Janssens","year":"2020","journal-title":"Int J Epidemiol."},{"key":"B26","doi-asserted-by":"publisher","first-page":"g7594","DOI":"10.1136\/bmj.g7594","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"350","author":"Collins","year":"2015","journal-title":"BMJ."},{"key":"B27","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1007\/s10461-014-0862-4","article-title":"HIV testing behaviors and perceptions of risk of HIV infection among MSM with main partners","volume":"19","author":"Stephenson","year":"2015","journal-title":"AIDS Behav."},{"key":"B28","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1071\/SH16001","article-title":"HIV incidence and associated risk factors in men who have sex with men in Mainland China: an updated systematic review and meta-analysis","volume":"13","author":"Zhang","year":"2016","journal-title":"Sex Health"},{"key":"B29","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1038\/sj.cr.7290359","article-title":"MSM and HIV\/AIDS in China","volume":"15","author":"Zhang","year":"2005","journal-title":"Cell Res."},{"key":"B30","doi-asserted-by":"publisher","first-page":"106420","DOI":"10.1016\/j.cmpb.2021.106420","article-title":"Using machine learning methods to predict hepatic encephalopathy in cirrhotic patients with unbalanced data","volume":"211","author":"Yang","year":"2021","journal-title":"Comput Methods Programs Biomed."},{"key":"B31","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1002\/jmv.25622","article-title":"Prevalence and protective factors of HIV and syphilis infection among men who have sex with men in Northwest China","volume":"92","author":"Wang","year":"2020","journal-title":"J Med Virol."},{"key":"B32","doi-asserted-by":"publisher","first-page":"e0404","DOI":"10.1097\/MD.0000000000010404","article-title":"HIV, syphilis and behavioral risk factors among men who have sex with men in a drug-using area of southwestern China: results of 3 cross-sectional surveys from 2013 to 2015","volume":"97","author":"Guanghua","year":"2018","journal-title":"Medicine."},{"key":"B33","doi-asserted-by":"publisher","first-page":"24039","DOI":"10.1038\/s41598-021-03430-5","article-title":"Research on expansion and classification of imbalanced data based on SMOTE algorithm","volume":"11","author":"Wang","year":"2021","journal-title":"Sci Rep."},{"key":"B34","doi-asserted-by":"publisher","first-page":"e696","DOI":"10.1016\/S2352-3018(19)30139-0","article-title":"Development and validation of an automated HIV prediction algorithm to identify candidates for pre-exposure prophylaxis: a modelling study","volume":"6","author":"Krakower","year":"2019","journal-title":"Lancet HIV."},{"key":"B35","doi-asserted-by":"publisher","first-page":"e688","DOI":"10.1016\/S2352-3018(19)30137-7","article-title":"Use of electronic health record data and machine learning to identify candidates for HIV pre-exposure prophylaxis: a modelling study","volume":"6","author":"Marcus","year":"2019","journal-title":"Lancet HIV."},{"key":"B36","doi-asserted-by":"publisher","first-page":"100203","DOI":"10.1016\/j.eclinm.2019.10.016","article-title":"Algorithmic prediction of HIV status using nation-wide electronic registry data","volume":"17","author":"Ahlstr\u00f6m","year":"2019","journal-title":"EClinicalMedicine."},{"key":"B37","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.jinf.2020.11.007","article-title":"Predicting the diagnosis of HIV and sexually transmitted infections among men who have sex with men using machine learning approaches","volume":"82","author":"Bao","year":"2021","journal-title":"J Infect."},{"key":"B38","doi-asserted-by":"publisher","first-page":"e25360","DOI":"10.2196\/25360","article-title":"The characteristics and risk factors of web-based sexual behaviors among men who have sex with men in eastern China: cross-sectional study","volume":"7","author":"Chen","year":"2021","journal-title":"JMIR Public Health Surveill."},{"key":"B39","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1097\/QAI.0000000000001580","article-title":"Using clinical notes and natural language processing for automated HIV risk assessment","volume":"77","author":"Feller","year":"2018","journal-title":"J Acquir Immune Defic Syndr."},{"key":"B40","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1038\/s41398-020-00957-5","article-title":"Machine learning for effectively avoiding overfitting is a crucial strategy for the genetic prediction of polygenic psychiatric phenotypes","volume":"10","author":"Takahashi","year":"2020","journal-title":"Transl Psychiatry."},{"key":"B41","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/978-3-030-85292-4_3","article-title":"Foundations of machine learning-based clinical prediction modeling: part II-generalization and overfitting","volume":"134","author":"Kernbach","year":"2022","journal-title":"Acta Neurochir Suppl."}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2022.967681\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T02:52:38Z","timestamp":1661395958000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2022.967681\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,25]]},"references-count":41,"alternative-id":["10.3389\/fpubh.2022.967681"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2022.967681","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,25]]},"article-number":"967681"}}