{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T18:58:38Z","timestamp":1787770718401,"version":"build-2784847793"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T00:00:00Z","timestamp":1764806400000},"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. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to develop a machine learning\u2013based model to predict recurrence risk after perianal abscess surgery, thereby supporting personalized follow-up and intervention strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Clinical data were collected from patients with perianal abscess who underwent surgery at Nanjing Hospital of Chinese Medicine, Affiliated to Nanjing University of Chinese Medicine between January 2022 and June 2023. Significant predictors were identified using the least absolute shrinkage and selection operator (LASSO) algorithm combined with multivariate logistic regression. The Synthetic Minority Over-sampling Technique (SMOTE) was applied to balance class distribution, and several machine learning (ML) algorithms were employed for model construction. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Model calibration was assessed using calibration curves. The effectiveness was evaluated through Decision Curve Analysis (DCA). Finally, the SHapley Additive ExPlanations (SHAP) were used to interpret the best-performing model and quantify the contribution of each predictor to its predictions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 737 patients with perianal abscess were included in the study. A history of diabetes, abscess space, and the aggregate index of systemic inflammation (AISI) were identified as the three strongest predictors of recurrence. Among all evaluated models, the CatBoost model showed the highest discriminatory power in the training set (AUC\u202f=\u202f0.821, 95%\n                      <jats:italic>CI<\/jats:italic>\n                      : 0.777\u20130.864), validation set (AUC\u202f=\u202f0.744, 95%\n                      <jats:italic>CI<\/jats:italic>\n                      : 0.616\u20130.872), and temporal validation set (AUC\u202f=\u202f0.735, 95%\n                      <jats:italic>CI<\/jats:italic>\n                      : 0.649\u20130.821).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The machine learning\u2013based model effectively identifies patients at high risk of recurrence after perianal abscess surgery. The CatBoost model achieved the best predictive performance, while SHAP analysis enhanced interpretability, supporting individualized patient management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2025.1722109","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T14:18:09Z","timestamp":1764857889000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Postoperative recurrence prediction model for perianal abscess using machine learning algorithms"],"prefix":"10.3389","volume":"13","author":[{"given":"Dawei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caixia","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiran","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaohua","family":"Huangfu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chungen","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qizhi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,12,4]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"e18855","DOI":"10.7717\/peerj.18855","article-title":"The bacterial composition signatures of perianal abscess and origin of infecting microbes","volume":"13","author":"Han","year":"2025","journal-title":"PeerJ"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1007\/s10350-004-0620-1","article-title":"Perianal abscess: a pilot study comparing packing with nonpacking of the abscess cavity","volume":"47","author":"Tonkin","year":"2004","journal-title":"Dis Colon Rectum"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1111\/ans.15801","article-title":"Surgical management of perianal abscess in neonates and infants","volume":"90","author":"Tan Tanny","year":"2020","journal-title":"ANZ J Surg"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1111\/acem.14151","article-title":"Comparison of the loop technique with incision and drainage for skin and soft tissue abscesses: a systematic review and meta-analysis","volume":"28","author":"Gottlieb","year":"2021","journal-title":"Acad Emerg Med"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1186\/s12876-023-02959-1","article-title":"Study on the factors influencing the prognosis after perianal abscess surgery","volume":"23","author":"Dong","year":"2023","journal-title":"BMC Gastroenterol"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"438","DOI":"10.19538\/j.cjps.issn1005-2208.2024.04.14","article-title":"Interpretation of the 2022 american society of colon and rectal surgeons clinical practice guidelines on perianal abscess, anal fistula, and rectovaginal fistula","volume":"4","author":"Zhou","year":"2024","journal-title":"Chin J Pract Surg"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1342892","DOI":"10.3389\/fped.2024.1342892","article-title":"Risk factors for therapy failure after incision and drainage alone for perianal abscesses in children","volume":"12","author":"Ding","year":"2024","journal-title":"Front Pediatr"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"2986","DOI":"10.1111\/cns.14237","article-title":"Machine learning for prediction of delirium in patients with extensive burns after surgery","volume":"29","author":"Ren","year":"2023","journal-title":"CNS Neurosci Ther"},{"key":"ref9","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":"ref10","doi-asserted-by":"publisher","first-page":"1271595","DOI":"10.3389\/fpubh.2023.1271595","article-title":"Disability risk prediction model based on machine learning among Chinese healthy older adults: results from the China health and retirement longitudinal study","volume":"11","author":"Han","year":"2023","journal-title":"Front Public Health"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1097\/DCR.0000000000002473","article-title":"The American society of colon and rectal surgeons clinical practice guidelines for the management of anorectal abscess, Fistula-in-Ano, and rectovaginal fistula","volume":"65","author":"Gaertner","year":"2022","journal-title":"Dis Colon Rectum"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1016\/j.ijsu.2011.06.002","article-title":"The impact of specialist experience in the surgical management of perianal abscesses","volume":"9","author":"Malik","year":"2011","journal-title":"Int J Surg"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s00384-012-1519-2","article-title":"Increased risk of diabetes following perianal abscess: a population-based follow-up study","volume":"28","author":"Wei","year":"2013","journal-title":"Int J Color Dis"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1093\/ehjdh\/ztae080","article-title":"Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis","volume":"6","author":"Liu","year":"2025","journal-title":"Eur Heart J Digit Health"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"e943666","DOI":"10.12659\/MSM.943666","article-title":"Machine learning-based prediction of helicobacter pylori infection study in adults","volume":"30","author":"Liu","year":"2024","journal-title":"Med Sci Monit"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"110028","DOI":"10.1016\/j.compbiomed.2025.110028","article-title":"Machine learning prediction models for multidrug-resistant organism infections in ICU ventilator-associated pneumonia patients: analysis using the MIMIC-IV database","volume":"190","author":"Cui","year":"2025","journal-title":"Comput Biol Med"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"E208","DOI":"10.1148\/rg.210142","article-title":"Perianal fistula and abscess: an imaging guide for beginners","volume":"42","author":"Ram\u00edrez Pedraza","year":"2022","journal-title":"Radiographics"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1148\/radiographics.20.3.g00mc15623","article-title":"MR imaging classification of perianal fistulas and its implications for patient management","volume":"20","author":"Morris","year":"2000","journal-title":"Radiographics"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1093\/aje\/kwk052","article-title":"Relaxing the rule of ten events per variable in logistic and cox regression","volume":"165","author":"Vittinghoff","year":"2007","journal-title":"Am J Epidemiol"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"103023","DOI":"10.1016\/j.eclinm.2024.103023","article-title":"Development and validation of a model to predict cognitive impairment in traumatic brain injury patients: a prospective observational study","volume":"80","author":"Yuan","year":"2025","journal-title":"EClinicalMedicine"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"m441","DOI":"10.1136\/bmj.m441","article-title":"Calculating the sample size required for developing a clinical prediction model","volume":"368","author":"Riley","year":"2020","journal-title":"BMJ"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"1163900","DOI":"10.3389\/fcvm.2023.1163900","article-title":"The aggregate index of systemic inflammation (AISI): a novel predictor for hypertension","volume":"10","author":"Xiu","year":"2023","journal-title":"Front Cardiovasc Med"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.14740\/jh1251","article-title":"Anorectal infections in neutropenic leukemia patients: a common clinical challenge","volume":"13","author":"Babakhanlou","year":"2024","journal-title":"J Hematol"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1111\/codi.16447","article-title":"Leukopenia is an independent risk factor for early postoperative complications following incision and drainage of anorectal abscess","volume":"25","author":"Khan","year":"2023","journal-title":"Color Dis"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"1590248","DOI":"10.3389\/fmed.2025.1590248","article-title":"Systemic immune-inflammatory biomarkers combined with the CRP-albumin-lymphocyte index predict surgical site infection following posterior lumbar spinal fusion: a retrospective study using machine learning","volume":"12","author":"Pang","year":"2025","journal-title":"Front Med (Lausanne)"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"221","DOI":"10.3969\/j.issn.1674-3245.2024.02.026","article-title":"Factors influencing wound healing after perianal abscess surgery and the construction of a nomogram risk mode","volume":"2","author":"Liu","year":"2024","journal-title":"Chin J Health Care Med"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1007\/s00384-020-03818-1","article-title":"Risk for developing perianal abscess in type 1 and type 2 diabetes and the impact of poor glycemic control","volume":"36","author":"Adamo","year":"2021","journal-title":"Int J Color Dis"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1111\/iwj.13665","article-title":"Novel fabrication of antibiotic containing multifunctional silk fibroin injectable hydrogel dressing to enhance bactericidal action and wound healing efficiency on burn wound: in vitro and in vivo evaluations","volume":"19","author":"Dong","year":"2022","journal-title":"Int Wound J"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1186\/s12876-025-03853-8","article-title":"Analysis of the distribution characteristics of infecting microorganisms in the wound tissue of patients with perianal abscess combined with infection and the influencing factors of wound healing","volume":"25","author":"Qiao","year":"2025","journal-title":"BMC Gastroenterol"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"13022","DOI":"10.1021\/acsnano.2c05557","article-title":"Bacterial growth-induced tobramycin smart release self-healing hydrogel for pseudomonas aeruginosa-infected burn wound healing","volume":"16","author":"Huang","year":"2022","journal-title":"ACS Nano"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1007\/s10350-007-0221-x","article-title":"MRSA-related perianal abscesses: an underrecognized disease entity","volume":"50","author":"Albright","year":"2007","journal-title":"Dis Colon Rectum"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"5648","DOI":"10.1177\/00031348231167397","article-title":"The not-so-distant future or just hype? Utilizing machine learning to predict 30-day post-operative complications in laparoscopic colectomy patients","volume":"89","author":"Velmahos","year":"2023","journal-title":"Am Surg"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"e553","DOI":"10.1016\/j.wneu.2022.03.060","article-title":"Using preoperative and intraoperative factors to predict the risk of surgical site infections after lumbar spinal surgery: a machine learning-based study","volume":"162","author":"Liu","year":"2022","journal-title":"World Neurosurg"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1093\/ckj\/sfaa188","article-title":"External validation of prognostic models: what, why, how, when and where?","volume":"14","author":"Ramspek","year":"2021","journal-title":"Clin Kidney J"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.jclinepi.2016.05.007","article-title":"Geographic and temporal validity of prediction models: different approaches were useful to examine model performance","volume":"79","author":"Austin","year":"2016","journal-title":"J Clin Epidemiol"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1044\/2023_JSLHR-23-00273","article-title":"Toward generalizable machine learning models in speech, language, and hearing sciences: estimating sample size and reducing overfitting","volume":"67","author":"Ghasemzadeh","year":"2024","journal-title":"J Speech Lang Hear Res"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/s12874-025-02463-y","article-title":"Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review","volume":"25","author":"Santos","year":"2025","journal-title":"BMC Med Res Methodol"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"103621","DOI":"10.1016\/j.jbi.2020.103621","article-title":"An empirical characterization of fair machine learning for clinical risk prediction","volume":"113","author":"Pfohl","year":"2021","journal-title":"J Biomed Inform"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1016\/j.csbj.2024.08.005","article-title":"Unveiling the black box: a systematic review of explainable artificial intelligence in medical image analysis","volume":"24","author":"Muhammad","year":"2024","journal-title":"Comput Struct Biotechnol J"}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2025.1722109\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T14:18:10Z","timestamp":1764857890000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2025.1722109\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,4]]},"references-count":39,"alternative-id":["10.3389\/fpubh.2025.1722109"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2025.1722109","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,4]]},"article-number":"1722109"}}