{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T10:51:15Z","timestamp":1782384675017,"version":"3.54.5"},"update-to":[{"DOI":"10.12688\/f1000research.179913.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000}}],"reference-count":32,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"<ns5:p>Background Chronic kidney disease (CKD) is a serious complication of type 2 diabetes (T2DM), particularly in low- and middle-income countries with limited access to early diagnosis. Predicting CKD risk using routine clinical data could enable earlier nephroprotective care. This study developed and internally validated a machine learning-based web application to predict incident CKD among T2DM patients in Indonesia\u2019s national health insurance program (Prolanis). Methods A machine learning prediction model was conducted using BPJS Prolanis data (2017\u20132023). Adults (\u226518\u00a0years) with T2DM and no prior CKD were included. Six algorithms (Logistic Regression, Random Forest, Decision Tree, XGBoost, LightGBM, CatBoost) were trained on 80% of the data and internally validated on the remaining 20% to predict CKD. Performance was assessed via accuracy, precision, recall, F1 score, and AUC. SHAP was used for interpretability. Results Among 7,581 individuals, 864 (11.4%) developed CKD. CatBoost achieved the best performance (AUC\u00a0=\u00a00.847, accuracy\u00a0=\u00a00.797, precision\u00a0=\u00a00.643, recall\u00a0=\u00a00.525, F1\u00a0=\u00a00.578). SHAP identified rapid-acting insulin analogues, amlodipine, furosemide, high blood urea nitrogen, and folic acid as key positive predictors. Advanced age and higher comorbidity burden increased risk, while chronic ischaemic heart disease and dental pulp diseases appeared protective\u2014likely due to healthcare utilization bias. A web-based risk calculator was developed. Conclusions The CatBoost-based web app demonstrated strong discriminative ability for predicting incident CKD in T2DM patients using routine claims data. This tool may support risk stratification in primary care settings across Indonesia and similar low-resource environments.<\/ns5:p>","DOI":"10.12688\/f1000research.179913.2","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T10:29:07Z","timestamp":1782383347000},"page":"690","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Web-based Machine Learning Model for Predicting Chronic Kidney Disease in Patients with Type 2 Diabetes Mellitus: A Multicenter Study"],"prefix":"10.12688","volume":"15","author":[{"given":"Lily","family":"Kresnowati","sequence":"first","affiliation":[{"name":"Doctoral Program of Public Health, Faculty of Public Health Semarang Central Java Universitas Diponegoro"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suhartono","family":"Suhartono","sequence":"additional","affiliation":[{"name":"Department of Environmental Health Semarang Central Java Diponegoro University School of Public Health"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2663-7918","authenticated-orcid":false,"given":"Zahroh","family":"Shaluhiyah","sequence":"additional","affiliation":[{"name":"Department of Health Promotion and Behavioral Science, Faculty of Public Health Semarang Central Java Universitas Diponegoro"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bagoes","family":"Widjanarko","sequence":"additional","affiliation":[{"name":"Department of Health Promotion and Behavioral Science, Faculty of Public Health Semarang Central Java Universitas Diponegoro"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7802-1328","authenticated-orcid":false,"given":"Faizul","family":"Hasan","sequence":"additional","affiliation":[{"name":"Faculty of Nursing Bangkok Bangkok Chulalongkorn University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2560","published-online":{"date-parts":[[2026,6,10]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"473-485","DOI":"10.1038\/s41581-024-00820-6","article-title":"Chronic kidney disease and the global public health agenda: an international consensus.","volume":"20","author":"A Francis","year":"2024","journal-title":"Nat. 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(Lausanne)."},{"key":"ref31","article-title":"Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation.","author":"D Powers","year":"2020","journal-title":"arXiv preprint arXiv:2010.16061."},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.19634415","author":"F Hasan","year":"2026","journal-title":"Zenodo."},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.19521819","article-title":"Tripod Checklist.","author":"F Hasan","year":"2026","journal-title":"Zenodo."}],"container-title":["F1000Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/f1000research.com\/articles\/15-690\/v2\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/15-690\/v2\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/15-690\/v2\/iparadigms","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T10:29:08Z","timestamp":1782383348000},"score":1,"resource":{"primary":{"URL":"https:\/\/f1000research.com\/articles\/15-690\/v2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,10]]},"references-count":32,"URL":"https:\/\/doi.org\/10.12688\/f1000research.179913.2","relation":{"has-version":[{"id-type":"doi","id":"10.12688\/f1000research.179913.1","asserted-by":"subject"}],"has-review":[{"id-type":"doi","id":"10.5256\/f1000research.202964.r492515","asserted-by":"subject"}]},"ISSN":["2046-1402"],"issn-type":[{"value":"2046-1402","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,10]]},"assertion":[{"value":"Indexed","URL":"https:\/\/f1000research.com\/articles\/15-690\/v2#article-reports","order":0,"name":"referee-status","label":"Referee status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.5256\/f1000research.202964.r492515, Cipta Pramana, Faculty of Medicine, Universitas Islam Negeri Walisongo Semarang, Semarang, Central Java, 25 Jun 2026, version 2, indexed","URL":"https:\/\/f1000research.com\/articles\/15-690\/v2#referee-response-492515","order":0,"name":"referee-response-492515","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.198473.r483727, Ewaldo Eder Carvalho Santana, State University of Maranh\u00e3o, S\u00e3o Lu\u00eds, 04 Jun 2026, version 1, 1 approved, 1 approved with reservations","URL":"https:\/\/f1000research.com\/articles\/15-690\/v1#referee-response-483727","order":1,"name":"referee-response-483727","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>faizul hasan<\/b>; \n<i>Posted: 04 Jun 2026<\/i>; We sincerely appreciate your time and insightful evaluation. We sincerely appreciate your favorable evaluation and are heartened that you considered the study scientifically robust, the methodologies suitable, and the results well-founded. Your acknowledgement of this work's significance for low- and middle-income countries is particularly important to us.","URL":"https:\/\/f1000research.com\/articles\/15-690\/v2#referee-comment-16357","order":2,"name":"referee-comment-16357","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"The author(s) declared that no grants were involved in supporting this work","order":3,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}