{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T19:18:49Z","timestamp":1788376729313,"version":"build-2803163510"},"reference-count":26,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004001","name":"Guizhou Science and Technology Department","doi-asserted-by":"publisher","award":["NO.(2017)5405"],"award-info":[{"award-number":["NO.(2017)5405"]}],"id":[{"id":"10.13039\/501100004001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004001","name":"Guizhou Science and Technology Department","doi-asserted-by":"publisher","award":["GZSYQCC[2015]006"],"award-info":[{"award-number":["GZSYQCC[2015]006"]}],"id":[{"id":"10.13039\/501100004001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004001","name":"Guizhou Science and Technology Department","doi-asserted-by":"publisher","award":["No.[2019]1197"],"award-info":[{"award-number":["No.[2019]1197"]}],"id":[{"id":"10.13039\/501100004001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004001","name":"Guizhou Science and Technology Department","doi-asserted-by":"publisher","award":["No.[2018]2794"],"award-info":[{"award-number":["No.[2018]2794"]}],"id":[{"id":"10.13039\/501100004001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>As the rate of percutaneous coronary intervention increases, in-stent restenosis (ISR) has become a burden. Random forest (RF) could be superior to logistic regression (LR) for predicting ISR due to its robustness. We developed an RF model and compared its performance with the LR one for predicting ISR. We retrospectively included 1501 patients (age: 64.0 \u00b1 10.3; male: 76.7%; ISR events: 279) who underwent coronary angiography at 9 to 18 months after implantation of 2<jats:sup>nd<\/jats:sup> generation drug-eluting stents. The data were randomly split into a pair of train and test datasets for model development and validation with 50 repeats. The predictive performance was assessed by the area under the curve (AUC) of the receiver operating characteristic (ROC). The RF models predicted ISR with larger AUC-ROCs of 0.829 \u00b1 0.025 compared to 0.784 \u00b1 0.027 of the LR models. The difference was statistically significant in 29 of the 50 repeats. The RF and LR models had similar sensitivity using the same cutoff threshold, but the specificity was significantly higher in the RF models, reducing 25% of the false positives. By removing the high leverage outliers, the LR models had comparable AUC-ROC to the RF models. Compared to the LR, the RF was more robust and significantly improved the performance for predicting ISR. It could cost-effectively identify patients with high ISR risk and help the clinical decision of coronary stenting.<\/jats:p>","DOI":"10.1371\/journal.pone.0268757","type":"journal-article","created":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T17:41:40Z","timestamp":1653327700000},"page":"e0268757","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":12,"title":["Random forest vs. logistic regression: Predicting angiographic in-stent restenosis after second-generation drug-eluting stent implantation"],"prefix":"10.1371","volume":"17","author":[{"given":"Zhi","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longhai","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianzong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8346-9899","authenticated-orcid":true,"given":"Fang","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,5,23]]},"reference":[{"key":"pone.0268757.ref001","article-title":"2018 ESC\/EACTS Guidelines on myocardial revascularization","author":"F-J Neumann","year":"2018","journal-title":"Eur Heart J"},{"key":"pone.0268757.ref002","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1016\/S0140-6736(18)30423-9","article-title":"Mortality after coronary artery bypass grafting versus percutaneous coronary intervention with stenting for coronary artery disease: a pooled analysis of individual patient data","volume":"391","author":"SJ Head","year":"2018","journal-title":"Lancet"},{"key":"pone.0268757.ref003","doi-asserted-by":"crossref","first-page":"2266","DOI":"10.1016\/j.jcin.2020.07.017","article-title":"Long-Term Prognostic Impact of Restenosis of the Unprotected Left Main Coronary Artery Requiring Repeat Revascularization","volume":"13","author":"J Wiebe","year":"2020","journal-title":"JACC Cardiovasc Interv"},{"key":"pone.0268757.ref004","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1161\/CIRCINTERVENTIONS.110.946939","article-title":"Predicting restenosis of drug-eluting stents placed in real-world clinical practice: derivation and validation of a risk model from the EVENT registry","volume":"3","author":"JM Stolker","year":"2010","journal-title":"Circ Cardiovasc Interv"},{"key":"pone.0268757.ref005","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1001\/jama.2016.7653","article-title":"Logistic Regression: Relating Patient Characteristics to Outcomes","volume":"316","author":"J Tolles","year":"2016","journal-title":"JAMA"},{"key":"pone.0268757.ref006","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1186\/s12859-018-2264-5","article-title":"Random forest versus logistic regression: a large-scale benchmark experiment","volume":"19","author":"R Couronn\u00e9","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"pone.0268757.ref007","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1111\/pace.14163","article-title":"Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement","volume":"44","author":"VT Truong","year":"2021","journal-title":"Pacing Clin Electrophysiol"},{"key":"pone.0268757.ref008","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1016\/j.cjca.2020.01.027","article-title":"Machine Learning to Predict Stent Restenosis Based on Daily Demographic, Clinical, and Angiographic Characteristics","volume":"36","author":"J Sampedro-G\u00f3mez","year":"2020","journal-title":"Can J Cardiol"},{"key":"pone.0268757.ref009","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1016\/S0140-6736(07)61602-X","article-title":"The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies","volume":"370","author":"E von Elm","year":"2007","journal-title":"Lancet"},{"key":"pone.0268757.ref010","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1161\/01.CIR.88.3.1310","article-title":"Defining coronary restenosis. 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