{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T04:45:49Z","timestamp":1779943549301,"version":"3.53.1"},"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T00:00:00Z","timestamp":1656547200000},"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\"><jats:title>Background and objective<\/jats:title><jats:p>Differentiating tropical infections are difficult due to its homogenous nature of clinical and laboratorial presentations among them. Sophisticated differential tests and prediction tools are better ways to tackle this issue. Here, we aimed to develop a clinician assisted decision making tool to differentiate the common tropical infections.<\/jats:p><\/jats:sec><jats:sec id=\"sec002\"><jats:title>Methodology<\/jats:title><jats:p>A cross sectional study through 9 item self-administered questionnaire were performed to understand the need of developing a decision making tool and its parameters. The most significant differential parameters among the identified infections were measured through a retrospective study and decision tree was developed. Based on the parameters identified, a multinomial logistic regression model and a machine learning model were developed which could better differentiate the infection.<\/jats:p><\/jats:sec><jats:sec id=\"sec003\"><jats:title>Results<\/jats:title><jats:p>A total of 40 physicians involved in the management of tropical infections were included for need analysis. Dengue, malaria, leptospirosis and scrub typhus were the common tropical infections in our settings. Sodium, total bilirubin, albumin, lymphocytes and platelets were the laboratory parameters; and abdominal pain, arthralgia, myalgia and urine output were the clinical presentation identified as better predictors. In multinomial logistic regression analysis with dengue as a reference revealed a predictability of 60.7%, 62.5% and 66% for dengue, malaria and leptospirosis, respectively, whereas, scrub typhus showed only 38% of predictability. The multi classification machine learning model observed to have an overall predictability of 55\u201360%, whereas a binary classification machine learning algorithms showed an average of 79\u201384% for one vs other and 69\u201388% for one vs one disease category.<\/jats:p><\/jats:sec><jats:sec id=\"sec004\"><jats:title>Conclusion<\/jats:title><jats:p>This is a first of its kind study where both statistical and machine learning approaches were explored simultaneously for differentiating tropical infections. Machine learning techniques in healthcare sectors will aid in early detection and better patient care.<\/jats:p><\/jats:sec>","DOI":"10.1371\/journal.pntd.0010455","type":"journal-article","created":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T17:25:23Z","timestamp":1656609923000},"page":"e0010455","update-policy":"https:\/\/doi.org\/10.1371\/journal.pntd.corrections_policy","source":"Crossref","is-referenced-by-count":14,"title":["Artificial intelligence in differentiating tropical infections: A step ahead"],"prefix":"10.1371","volume":"16","author":[{"given":"Shreelaxmi","family":"Shenoy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asha K.","family":"Rajan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammed","family":"Rashid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viji 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