{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T13:39:26Z","timestamp":1788615566832,"version":"build-2803163510"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"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 and objective<\/jats:title>\n                    <jats:p>\n                      Leprosy is a chronic infectious disease caused by\n                      <jats:italic>Mycobacterium leprae<\/jats:italic>\n                      that is often misdiagnosed. This study aimed to identify factors associated with leprosy misdiagnosis and develop and compare machine learning (ML) models to predict the risk of misdiagnosis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective analysis was conducted on clinical and epidemiological data in 486 diagnosed leprosy patients. The outcome was a binary variable to indicate whether a patient had experienced a prior misdiagnosis. Features analyzed included sociodemographic factors, clinical characteristics, and epidemiological exposures. LASSO regression analysis performed feature selection. Class imbalance was handled using synthetic minority oversampling technique. Nine ML models were trained and validated with a 80\u201320 data split. The best model performance was evaluated based on AUC-ROC, sensitivity, and specificity. Important features were interpreted using the SHapley Additive exPlanation (SHAP) technique.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 486 leprosy patients, 159 (32.7%) experienced misdiagnoses. Nineteen features were selected for model development. The best-performing model was Neural Network, which demonstrated the most balanced performance (AUC: 0.79 and 0.68, sensitivity: 0.93 and 0.78, specificity: 0.68 and 0.57 in train and test, respectively). The SHAP analysis identified key predictors associated with the detection of leprosy misdiagnosis, including mode of detection, aspartate aminotransferase level, gender, and the presence of skin lesions. In addition, ethnicity, education, leprosy reaction, household contact with an active case, and source of infection also contributed to the detection of leprosy misdiagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Applying ML to clinical data can effectively identify leprosy patients at high risk of being misdiagnosed using clinical, social and epidemiology characteristics. A ML-based support tool could aid frontline healthcare providers to reduce overlooking leprosy diseases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1785606","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T05:54:22Z","timestamp":1778219662000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine learning methods to predict leprosy misdiagnosis in Yunnan Province, People's Republic of China"],"prefix":"10.3389","volume":"14","author":[{"given":"Yingwu","family":"Guo","sequence":"first","affiliation":[{"name":"Department of Dermatology Venereology, First Affiliated Hospital of Kunming Medical University","place":["Kunming, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijiao","family":"Yin","sequence":"additional","affiliation":[{"name":"Wenshan Prefecture Dermatology Prevention and Treatment Institute (Wenshan Prefecture Dermatology Specialist Hospital)","place":["Wenshan, Yunnan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hailong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Basic Medical Sciences, Kunming Medical University","place":["Kunming, Yunnan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Dermatology Venereology, First Affiliated Hospital of Kunming Medical University","place":["Kunming, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiufeng","family":"Yu","sequence":"additional","affiliation":[{"name":"Wenshan Prefecture Dermatology Prevention and Treatment Institute (Wenshan Prefecture Dermatology Specialist Hospital)","place":["Wenshan, Yunnan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Wenshan Prefecture Dermatology Prevention and Treatment Institute (Wenshan Prefecture Dermatology Specialist Hospital)","place":["Wenshan, Yunnan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Wenshan Prefecture Dermatology Prevention and Treatment Institute (Wenshan Prefecture Dermatology Specialist Hospital)","place":["Wenshan, Yunnan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fushuai","family":"Zhao","sequence":"additional","affiliation":[{"name":"Wenshan Prefecture Dermatology Prevention and Treatment Institute (Wenshan Prefecture Dermatology Specialist Hospital)","place":["Wenshan, Yunnan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengqing","family":"Lu","sequence":"additional","affiliation":[{"name":"Wenshan Prefecture Dermatology Prevention and Treatment Institute (Wenshan Prefecture Dermatology Specialist Hospital)","place":["Wenshan, Yunnan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiaojing","family":"He","sequence":"additional","affiliation":[{"name":"Wenshan Prefecture Dermatology Prevention and Treatment Institute (Wenshan Prefecture Dermatology Specialist Hospital)","place":["Wenshan, Yunnan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Han","sequence":"additional","affiliation":[{"name":"Wenshan Prefecture Dermatology Prevention and Treatment Institute (Wenshan Prefecture Dermatology Specialist Hospital)","place":["Wenshan, Yunnan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Wenshan Prefecture Dermatology Prevention and Treatment Institute (Wenshan Prefecture Dermatology Specialist Hospital)","place":["Wenshan, Yunnan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youhong","family":"Liu","sequence":"additional","affiliation":[{"name":"Wenshan Prefecture Dermatology Prevention and Treatment Institute (Wenshan Prefecture Dermatology Specialist Hospital)","place":["Wenshan, Yunnan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Ye","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Dermatology Venereology, First Affiliated Hospital of Kunming Medical University","place":["Kunming, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"B1","unstructured":"Leproy"},{"key":"B2","doi-asserted-by":"publisher","first-page":"120","DOI":"10.25259\/IJMR_195_2025","article-title":"Leprosy research updates: shaping the future of global health","volume":"161","author":"Deps","year":"2025","journal-title":"Indian J Med Res"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e0010756","DOI":"10.1371\/journal.pntd.0010756","article-title":"Delayed detection of leprosy cases: a systematic review of healthcare-related factors","volume":"16","author":"Dharmawan","year":"2022","journal-title":"PLoS Negl Trop Dis"},{"key":"B4","doi-asserted-by":"publisher","first-page":"e34714","DOI":"10.1097\/MD.0000000000034714","article-title":"Analysis of misdiagnosed or delayed-diagnosed Leprosy bacillus infection from 1990 to 2020 with a prophet time series prediction in Hubei Province, China","volume":"102","author":"Chen","year":"2023","journal-title":"Medicine"},{"key":"B5","doi-asserted-by":"publisher","first-page":"8652062","DOI":"10.1155\/2022\/8652062","article-title":"Leprosy: a review of epidemiology, clinical diagnosis, and management","volume":"2022","author":"Chen","year":"2022","journal-title":"J Trop Med"},{"key":"B6","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1186\/s12913-025-12334-3","article-title":"Healthcare performance of leprosy management in peripheral health facilities of Dhanusa and Mahottari, Nepal","volume":"25","author":"Mahato","year":"2025","journal-title":"BMC Health Serv Res"},{"key":"B7","doi-asserted-by":"publisher","first-page":"176","DOI":"10.47276\/lr.81.3.176","article-title":"A big challenge in case finding at low endemic situation: analysis on 1462 new leprosy patients detected in China in 2007","volume":"81","author":"Shen","year":"2010","journal-title":"Lepr Rev"},{"key":"B8","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1038\/s41746-024-01033-8","article-title":"Deep learning models across the range of skin disease","volume":"7","author":"Venkatesh","year":"2024","journal-title":"NPJ Digit Med"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13010180","article-title":"Artificial intelligence on diagnostic aid of leprosy: a systematic literature review","author":"Fernandes","year":"2023","journal-title":"J Clin Med; 13 Epub ahead of print 28 December"},{"key":"B10","doi-asserted-by":"publisher","first-page":"50131","DOI":"10.3390\/tropicalmed10050131","article-title":"Evaluating machine learning models for predicting late leprosy diagnosis by physical disability grade in Brazil [Epub ahead of print 12 May 2025]","volume":"10","author":"Freitas LRS","year":"2025","journal-title":"Trop Med Infect Dis"},{"key":"B11","doi-asserted-by":"publisher","first-page":"e37531","DOI":"10.2196\/37531","article-title":"Using artificial intelligence as a diagnostic decision support tool in skin disease: protocol for an observational prospective cohort study","volume":"11","author":"Escal\u00e9-Besa","year":"2022","journal-title":"JMIR Res Protoc"},{"key":"B12","doi-asserted-by":"publisher","first-page":"531","DOI":"10.3390\/diagnostics13030531","article-title":"Impact of Misdiagnosis in Case-Control Studies of Myalgic Encephalomyelitis\/Chronic Fatigue Syndrome","volume":"13","author":"Malato","year":"2023","journal-title":"Diagnostics"},{"key":"B13","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1002\/sam.11583","article-title":"Optimal ratio for data splitting","volume":"15","author":"Joseph","year":"2022","journal-title":"Stat Anal Data Min"},{"key":"B14","doi-asserted-by":"publisher","first-page":"e42690","DOI":"10.1097\/MD.0000000000042690","article-title":"Comparison of machine learning models for predicting stroke risk in hypertensive patients: Lasso regression model, random forest model, Boruta algorithm model, and Boruta algorithm combined with Lasso regression model","volume":"104","author":"Huang","year":"2025","journal-title":"Medicine"},{"key":"B15","doi-asserted-by":"publisher","first-page":"e0266267","DOI":"10.1371\/journal.pone.0266267","article-title":"Regression shrinkage and selection via least quantile shrinkage and selection operator","volume":"18","author":"Daneshvar","year":"2023","journal-title":"PLoS ONE"},{"key":"B16","doi-asserted-by":"publisher","first-page":"13686","DOI":"10.1109\/ACCESS.2025.3531662","article-title":"Imbalanced data problem in machine learning: a review","volume":"13","author":"Altalhan","year":"2025","journal-title":"IEEE Access"},{"key":"B17","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/978-981-97-0448-4_3","article-title":"Building Predictive Models with Machine Learning BT - Data Analytics and Machine Learning: Navigating the Big Data Landscape","author":"Gupta","year":""},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1002\/0470011815.b2a10030","author":"Hosmer","year":"2004","journal-title":"Applied logistic regression"},{"key":"B19","article-title":"Confusion matrix","author":"Murphy","year":"2019","journal-title":"Radiopaediaorg Epub ahead of print"},{"key":"B20","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1093\/jamia\/ocad006","article-title":"Assessing the net benefit of machine learning models in the presence of resource constraints","volume":"30","author":"Singh","year":"2023","journal-title":"J Am Med Inform Assoc"},{"key":"B21","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1093\/oxfordjournals.aje.a112510","article-title":"Estimating prevalence from the results of a screening test","volume":"107","author":"Rogan","year":"1978","journal-title":"Am J Epidemiol"},{"key":"B22","first-page":"4768","article-title":"A unified approach to interpreting model predictions","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Lundberg","year":""},{"key":"B23","doi-asserted-by":"publisher","first-page":"e1011504","DOI":"10.1371\/journal.pcbi.1011504","article-title":"UNNT: a novel Utility for comparing Neural Net and Tree-based models","volume":"20","author":"Gutta","year":"2024","journal-title":"PLoS Comput Biol"},{"key":"B24","doi-asserted-by":"publisher","first-page":"395","DOI":"10.3389\/fmicb.2019.00395","article-title":"Artificial Neural Networks for Prediction of Tuberculosis Disease","volume":"10","author":"Khan","year":"2019","journal-title":"Front Microbiol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"1233220","DOI":"10.3389\/fmed.2023.1233220","article-title":"Prediction of the occurrence of leprosy reactions based on Bayesian networks","volume":"10","author":"de Andrade Rodrigues","year":"2023","journal-title":"Front Med"},{"key":"B26","doi-asserted-by":"publisher","first-page":"522","DOI":"10.3390\/bs13070522","article-title":"A deep learning neural network to classify obesity risk in portuguese adolescents based on physical fitness levels and body mass index percentiles: insights for national health policies","volume":"13","author":"Forte","year":"2023","journal-title":"Behav Sci"},{"key":"B27","doi-asserted-by":"publisher","first-page":"e0324632","DOI":"10.1371\/journal.pone.0324632","article-title":"Machine learning-based prediction model for cognitive impairment risk in patients with chronic kidney disease","volume":"20","author":"Cao","year":"2025","journal-title":"PLoS ONE"},{"key":"B28","first-page":"194","volume":"59","author":"Kumar","year":"1987","journal-title":"Indian J Lepr"},{"key":"B29","doi-asserted-by":"publisher","first-page":"121","DOI":"10.2147\/CCID.S502544","article-title":"Misdiagnosis of Leprosy with Severe Reversal Reaction as Psoriatic Arthritis: a case report and literature review","volume":"18","author":"Gunawan","year":"2025","journal-title":"Clin Cosmet Investig Dermatol"},{"key":"B30","doi-asserted-by":"publisher","first-page":"e1012550","DOI":"10.1371\/journal.pcbi.1012550","article-title":"On the usage of artificial intelligence in leprosy care: A systematic literature review","volume":"21","author":"de Andrade","year":"2025","journal-title":"PLoS Comput Biol"}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1785606\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T05:54:23Z","timestamp":1778219663000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1785606\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,8]]},"references-count":30,"alternative-id":["10.3389\/fpubh.2026.1785606"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2026.1785606","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,8]]},"article-number":"1785606"}}