{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T01:52:26Z","timestamp":1792547546002,"version":"4.5.0"},"update-to":[{"DOI":"10.1371\/journal.pntd.0012026","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T00:00:00Z","timestamp":1715644800000}}],"reference-count":14,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosntds.org"],"crossmark-restriction":false},"short-container-title":["PLoS Negl Trop Dis"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Introduction<\/jats:title>\n<jats:p>Chagas disease is a severe parasitic illness that is prevalent in Latin America and often goes unaddressed. Early detection and treatment are critical in preventing the progression of the illness and its associated life-threatening complications. In recent years, machine learning algorithms have emerged as powerful tools for disease prediction and diagnosis.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>In this study, we developed machine learning algorithms to predict the risk of Chagas disease based on five general factors: age, gender, history of living in a mud or wooden house, history of being bitten by a triatomine bug, and family history of Chagas disease. We analyzed data from the Retrovirus Epidemiology Donor Study (REDS) to train five popular machine learning algorithms. The sample comprised 2,006 patients, divided into 75% for training and 25% for testing algorithm performance. We evaluated the model performance using precision, recall, and AUC-ROC metrics.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>The Adaboost algorithm yielded an AUC-ROC of 0.772, a precision of 0.199, and a recall of 0.612. We simulated the decision boundary using various thresholds and observed that in this dataset a threshold of 0.45 resulted in a 100% recall. This finding suggests that employing such a threshold could potentially save 22.5% of the cost associated with mass testing of Chagas disease.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>Our findings highlight the potential of applying machine learning to improve the sensitivity and effectiveness of Chagas disease diagnosis and prevention. Furthermore, we emphasize the importance of integrating socio-demographic and environmental factors into neglected disease prediction models to enhance their performance.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pntd.0012026","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T18:27:48Z","timestamp":1713292068000},"page":"e0012026","update-policy":"https:\/\/doi.org\/10.1371\/journal.pntd.corrections_policy","source":"Crossref","is-referenced-by-count":6,"title":["Machine learning for predicting Chagas disease infection in rural areas of Brazil"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9875-2339","authenticated-orcid":true,"given":"Fabio","family":"De Rose Ghilardi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriel","family":"Silva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thallyta Maria","family":"Vieira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ariela","family":"Mota","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana Luiza","family":"Bierrenbach","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renata Fiuza","family":"Damasceno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lea Campos de","family":"Oliveira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandre","family":"Dias Porto Chiavegatto Filho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ester","family":"Sabino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"key":"pntd.0012026.ref001","author":"World Health Organization","journal-title":"Chagas disease (also known as American trypanosomiasis): Key facts."},{"key":"pntd.0012026.ref002","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.actatropica.2016.01.002","article-title":"A global systematic review of Chagas disease prevalence among migrants","volume":"156","author":"E Conners E","year":"2016","journal-title":"Acta Trop"},{"key":"pntd.0012026.ref003","first-page":"6","article-title":"Corbellino M Chagas disease in Europe: A review for the internist in the globalized world. 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Dis."},{"key":"pntd.0012026.ref007","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"Kegelmeyer W P SMOTE: synthetic minority over-sampling technique","volume":"16","author":"V Chawla N","journal-title":"Journal of artificial intelligence research"},{"key":"pntd.0012026.ref008","doi-asserted-by":"crossref","unstructured":"Schapire R E Explaining adaboost. In Empirical inference, 37\u201352. Springer.","DOI":"10.1007\/978-3-642-41136-6_5"},{"key":"pntd.0012026.ref009","first-page":"3146","article-title":"Liu T-Y Lightgbm: A highly efficient gradient boosting decision tree","volume":"30","author":"G Ke","journal-title":"Advances in Neural Information Processing Systems"},{"key":"pntd.0012026.ref010","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C XGBoost: A Scalable Tree Boosting System. in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 2016. 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