{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T17:35:35Z","timestamp":1789752935800,"version":"4.0.1"},"reference-count":23,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education, Cultural, Research and Technology","award":["Number 075\/E5\/PG.02.00.PL\/2023 date12 April 2023; Number : 0254.8\/LL5-INT\/AL.04\/2023 date 17 April 2023; and Number 004\/WCR\/LPPM UAD\/IV\/2023."],"award-info":[{"award-number":["Number 075\/E5\/PG.02.00.PL\/2023 date12 April 2023; Number : 0254.8\/LL5-INT\/AL.04\/2023 date 17 April 2023; and Number 004\/WCR\/LPPM UAD\/IV\/2023."]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Indonesia is still the second-highest tuberculosis burden country in the world. The antituberculosis adverse drug reaction and adherence may influence the success of treatment. The objective of this study is to define the model for predicting the adherence in tuberculosis patients, based on the increased level of liver enzymes. The longitudinal study using adult tuberculosis patients treated with the first line of antituberculosis was conducted prospectively. The pregnant women and patients with complications such as gout, diabetes mellitus, liver disorder and HIV were excluded. We measured the total bilirubin, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) and adherence over the 2nd, 4th, and 6th months of the treatment. We used the ORANGE Data mining as the machine learning to predict the adherence. We recruited 201 patients, whereas the male participants and less than 61 years old as the dominant participants. Around 33%, 35% and 35% tuberculosis patients experienced the increase level of bilirubine, ALT and AST, respectively. There were significant differences in ALT and AST between good and poor adherence groups, especially in the female patients. The Neural Network and Random Forests were the most suitable models to predict tuberculosis patients\u2019 adherence with good Area Under The Curve (AUC).<\/jats:p>","DOI":"10.1371\/journal.pone.0315912","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T18:35:17Z","timestamp":1737743717000},"page":"e0315912","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2638-6664","authenticated-orcid":true,"given":"Dyah Aryani","family":"Perwitasari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Imaniar Noor","family":"Faridah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haafizah","family":"Dania","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9104-548X","authenticated-orcid":true,"given":"Didik","family":"Setiawan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Triantoro","family":"Safaria","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"pone.0315912.ref001","unstructured":"Anonymous. Global Tuberculosis Report [Internet]. WHO. 2023 [cited 2024 Jul 27]. Available from: https:\/\/iris.who.int\/bitstream\/handle\/10665\/373828\/9789240083851-eng.pdf?sequence=1"},{"key":"pone.0315912.ref002","unstructured":"Anonymous. Press Briefing: Tuberculosis World Day 2023 [Internet]. Ministry of Health, Indonesia. 2023 [cited 2024 Jul 27]. 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Tuberkulosis, Pedoman Diagnosis dan Penatalaksanaan di Indonesia (Tuberculosis, Diagnosis and Management in Indonesia). Perhimpunan Dokter Paru Indonesia (PDPI); 2021."},{"issue":"5","key":"pone.0315912.ref016","doi-asserted-by":"crossref","first-page":"e063926","DOI":"10.1136\/bmjopen-2022-063926","article-title":"Patient adherence to tuberculosis treatment in the Indian subcontinent: systematic review and meta-synthesis of qualitative research","volume":"13","author":"K Shringarpure","year":"2023","journal-title":"BMJ Open"},{"key":"pone.0315912.ref017","unstructured":"Anonymous. National Strategy for Tuberculosis ELimination [Internet]. Jakarta; 2020. Available from: https:\/\/tbindonesia.or.id\/wp-content\/uploads\/2021\/06\/NSP-TB-2020-2024-Ind_Final_-BAHASA.pdf"},{"issue":"7","key":"pone.0315912.ref018","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1080\/17441692.2016.1240823","article-title":"Surveillance or support: The experience of direct observation during tuberculosis treatment","volume":"13","author":"I Bojorquez","year":"2018","journal-title":"Glob Public Health"},{"issue":"7","key":"pone.0315912.ref019","doi-asserted-by":"crossref","first-page":"934","DOI":"10.5588\/ijtld.12.0782","article-title":"Hepatotoxicity due to first-line antituberculosis drugs: a five-year experience in a Taiwan medical centre","volume":"17","author":"C-C Shu","year":"2013","journal-title":"Int J Tuberc lung Dis Off J Int Union against Tuberc Lung Dis"},{"issue":"4","key":"pone.0315912.ref020","doi-asserted-by":"crossref","first-page":"e26993","DOI":"10.2196\/26993","article-title":"Machine Learning and Medication Adherence: Scoping Review","volume":"2","author":"A Bohlmann","year":"2021","journal-title":"JMIRx med"},{"issue":"3","key":"pone.0315912.ref021","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1049\/htl.2016.0041","article-title":"Predicting adherence of patients with HF through machine learning techniques","volume":"3","author":"GS Karanasiou","year":"2016","journal-title":"Healthc Technol Lett"},{"issue":"8","key":"pone.0315912.ref022","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1007\/s11517-011-0785-4","article-title":"Prediction of persistence of combined evidence-based cardiovascular medications in patients with acute coronary syndrome after hospital discharge using neural networks","volume":"49","author":"V Bourd\u00e8s","year":"2011","journal-title":"Med Biol Eng Comput"},{"key":"pone.0315912.ref023","doi-asserted-by":"crossref","first-page":"107668","DOI":"10.1016\/j.drugalcdep.2019.107668","article-title":"Predictors of adherence to nicotine replacement therapy: Machine learning evidence that perceived need predicts medication use","volume":"205","author":"N Kim","year":"2019","journal-title":"Drug Alcohol Depend"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0315912","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T18:35:28Z","timestamp":1737743728000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0315912"}},"subtitle":[],"editor":[{"given":"Frederick","family":"Quinn","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,1,24]]},"references-count":23,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1,24]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0315912","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,24]]}}}