{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T06:34:07Z","timestamp":1793428447626,"version":"4.5.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"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. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Cholelithiasis, commonly known as Gallstone disease, occurs when hardened deposits form in the gallbladder or bile ducts. It affects millions of people worldwide and is especially common in women. While many people may not experience any symptoms, symptomatic cases can present with acute cholecystitis and other complications such as pancreatitis and even gallbladder cancer. However, this disease presents a clinical challenge due to its variable symptoms and risk of serious complications. Therefore, early prediction of gallstones is essential for timely intervention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>Thus, our study presents a novel approach for predicting gallstones. In this study, we have presented a Rotational Forest (RoF) classifier optimized using the Bald Eagle Search (BES) algorithm for gallstone prediction based on a tabular dataset. Our research has been conducted across two frameworks: using RoF alone and using RoF with the BES algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result<\/jats:title>\n                    <jats:p>While using RoF alone, an accuracy of 78% and an AUC of 0.867 was obtained using all features. An accuracy of 75.78% and an AUC of 0.860 were obtained for RoF with the BES algorithm using only 17 features. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) analysis has distinguished CRP, Vitamin D, Obesity, HGB, and BM as the most dominant features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Likewise, we have also compared our work with other novel works and validated the performance of our model for the prediction of gallstones.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2025.1727559","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T10:09:47Z","timestamp":1768558187000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Metaheuristic-based gallstone classification using rotational forest explained with SHAP"],"prefix":"10.3389","volume":"7","author":[{"given":"Keshika","family":"Shrestha","sequence":"first","affiliation":[{"name":"Electronics and Communication Engineering Discipline, Khulna University","place":["Khulna, Bangladesh"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Proshenjit","family":"Sarker","sequence":"additional","affiliation":[{"name":"Electronics and Communication Engineering Discipline, Khulna University","place":["Khulna, Bangladesh"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun-Jiat","family":"Tiang","sequence":"additional","affiliation":[{"name":"Centre for Wireless Technology, CoE for Intelligent Network, Faculty of Artificial Intelligence and Engineering, Multimedia University","place":["Cyberjaya, Selangor, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah-Al","family":"Nahid","sequence":"additional","affiliation":[{"name":"Electronics and Communication Engineering Discipline, Khulna University","place":["Khulna, Bangladesh"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1053\/j.gastro.2009.02.038","article-title":"Burden of digestive diseases in the United States part III: liver, biliary tract, and pancreas","volume":"136","author":"Everhart","year":"2009","journal-title":"Gastroenterology"},{"key":"B2","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/S0140-6736(06)69044-2","article-title":"Cholesterol gallstone disease","volume":"368","author":"Portincasa","year":"2006","journal-title":"Lancet"},{"key":"B3","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1002\/hep.1840070520","article-title":"A population study on the prevalence of gallstone disease: the Sirmione study","volume":"7","author":"Barbara","year":"1987","journal-title":"Hepatology"},{"key":"B4","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1016\/j.bpg.2006.05.004","article-title":"Epidemiology of gallbladder stone disease","volume":"20","author":"Shaffer","year":"2006","journal-title":"Best Pract Res Clin Gastroenterol"},{"key":"B5","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1007\/s10354-006-0346-x","article-title":"Gender and gallstone disease","volume":"156","author":"Novacek","year":"2006","journal-title":"Wiener Medizinische Wochenschrift"},{"key":"B6","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1002\/hep.510260401","article-title":"Factors associated with gallstone disease in the MICOL experience","volume":"26","author":"Attili","year":"1997","journal-title":"Hepatology"},{"key":"B7","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1136\/gut.32.3.316","article-title":"Symptomatic and silent gall stones in the community","volume":"32","author":"Heaton","year":"1991","journal-title":"Gut"},{"key":"B8","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","article-title":"Scalable and accurate deep learning with electronic health records","volume":"1","author":"Rajkomar","year":"2018","journal-title":"NPJ Digit Med"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1511.05942","article-title":"Doctor AI: predicting clinical events via recurrent neural networks","author":"Choi","year":"2016","journal-title":"arXiv"},{"key":"B10","doi-asserted-by":"publisher","first-page":"509","DOI":"10.2147\/IJGM.S507013","article-title":"Relative fat mass and physical indices as predictors of gallstone formation: insights from machine learning and logistic regression","volume":"18","author":"Deng","year":"2025","journal-title":"Int J Gen Med"},{"key":"B11","doi-asserted-by":"publisher","first-page":"e37258","DOI":"10.1097\/md.0000000000037258","article-title":"Early prediction of gallstone disease with a machine learning-based method from bioimpedance and laboratory data","volume":"103","author":"Esen","year":"2024","journal-title":"Medicine (Baltimore)"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13017-024-00571-6","article-title":"An explainable predictive machine learning model of gangrenous cholecystitis based on clinical data: a retrospective single center study","volume":"20","author":"Ma","year":"2025","journal-title":"World J Emerg Surg"},{"key":"B13","doi-asserted-by":"crossref","DOI":"10.21203\/rs.3.rs-4508424\/v1","article-title":"Machine learning models based on dietary data to predict gallstones: NHANES 2017-2020","author":"Shao","year":"2024"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.3390\/rs9111205","article-title":"An improved rotation forest for multi-feature remote-sensing imagery classification","volume":"9","author":"Xiu","year":"2017","journal-title":"Remote Sens (Basel)"},{"key":"B15","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1007\/s00521-015-2103-9","article-title":"Breast cancer diagnosis using GA feature selection and rotation forest","volume":"28","author":"Ali\u0103\u017aKovi\u0107","year":"2017","journal-title":"Neural Comput. Appl"},{"key":"B16","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1134\/S0361768815050059","article-title":"Ultrasound image texture analysis for liver fibrosis stage diagnostics","volume":"41","author":"Kvostikov","year":"2015","journal-title":"Programm Comput Softw"},{"key":"B17","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/j.compbiomed.2008.02.007","article-title":"Cancer classification using rotation forest","volume":"38","author":"Liu","year":"2008","journal-title":"Comput Biol Med"},{"key":"B18","doi-asserted-by":"publisher","first-page":"5524852","DOI":"10.1155\/2022\/5524852","article-title":"An efficient rotation forest-based ensemble approach for predicting severity of Parkinson\u2019s disease","volume":"2022","author":"Sheikhi","year":"2022","journal-title":"J Healthc Eng"},{"key":"B19","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1177\/0161734618787447","article-title":"Preliminary study of chronic liver classification on ultrasound images using an ensemble model","volume":"40","author":"Bharti","year":"2018","journal-title":"Ultrason Imaging"},{"key":"B20","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/1460458212446096","article-title":"On optimal settings of classification tree ensembles for medical decision support","volume":"19","author":"Budnik","year":"2013","journal-title":"Health Informatics J"},{"key":"B21","doi-asserted-by":"publisher","first-page":"2237","DOI":"10.1007\/s10462-019-09732-5","article-title":"Novel meta-heuristic bald eagle search optimisation algorithm","volume":"53","author":"Alsattar","year":"2020","journal-title":"Artif Intell Rev"},{"key":"B22","doi-asserted-by":"publisher","first-page":"2385310","DOI":"10.1080\/21642583.2024.2385310","article-title":"Bald eagle search algorithm: a comprehensive review with its variants and applications","volume":"12","author":"El-Shorbagy","year":"2024","journal-title":"Syst Sci Control Eng"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1109\/TPAMI.2006.211","article-title":"Rotation forest: a new classifier ensemble method","volume":"28","author":"Rodriguez","year":"2006","journal-title":"IEEE Ttrans Pattern Anal Mach Intell"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1705.07874","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017","journal-title":"arXiv"},{"key":"B25","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat Mach Intell"},{"key":"B26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s44196-025-00740-8","article-title":"Analysis of machine learning algorithms for real-time gallbladder stone identification from ultrasound images in clinical decision support systems","volume":"18","author":"Hong","year":"2025","journal-title":"Int J Comput Intell Syst"},{"key":"B27","article-title":"\u201cBayesian Hybrid Machine Learning of Gallstone Risk,\u201d arXiv preprint arXiv:2506.14561","author":"Chakraborty","year":"2025"},{"key":"B28","doi-asserted-by":"publisher","first-page":"6091","DOI":"10.3390\/jcm14176091","article-title":"Determination of malignancy risk factors using gallstone data and comparing machine learning methods to predict malignancy","volume":"14","author":"Cetin","year":"2025","journal-title":"J Clin Med"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1727559\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T07:35:28Z","timestamp":1771918528000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1727559\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,16]]},"references-count":28,"alternative-id":["10.3389\/fdgth.2025.1727559"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2025.1727559","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,16]]},"article-number":"1727559"}}