{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T14:23:27Z","timestamp":1793197407069,"version":"4.5.0"},"reference-count":29,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T00:00:00Z","timestamp":1661731200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T00:00:00Z","timestamp":1661731200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T00:00:00Z","timestamp":1661731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["2021R1A2C1092657"],"award-info":[{"award-number":["2021R1A2C1092657"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2021R1A2C1092657"],"award-info":[{"award-number":["2021R1A2C1092657"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knee surg. sports traumatol. arthrosc."],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Several methods have been developed to prevent lateral hinge fractures (LHFs), using only classic statistical models. Machine learning is under the spotlight because of its ability to analyze various weights and model nonlinear relationships. The purpose of this study was to create a machine learning model that predicts LHF with high predictive performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Data were collected from a total of 439\u00a0knees with medial osteoarthritis (OA) treated with Medial open wedge high tibial osteotomy (MOW\u2010HTO) from March 2014 to February 2020. The patient data included age, sex, height, and weight. Preoperative, determined, and modifiable factors were categorized using X\u2010ray and CT data to create ensemble models with better predictive performance. Among the 57 ensemble models, which is the total number of possible combinations with six models, the model with the highest area under curve (AUC) or F1\u2010score was selected as the final ensemble model. Gain feature importance analysis and the Shapley additive explanations (SHAP) feature explanation were performed on the best models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The ensemble model with the highest AUC was a combination of a light gradient boosting machine (LGBM) and multilayer perceptron (MLP) (AUC\u2009=\u20090.992). The ensemble model with the highest F1\u2010score was the model that combined logistic regression (LR) and MLP (F1\u2010score\u2009=\u20090.765). Distance <jats:italic>X<\/jats:italic> was the most predictive feature in the results of both model interpretation analyses.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Two types of ensemble models, LGBM with MLP and LR with MLP, were developed as machine learning models to predict LHF with high predictive performance. Using these models, surgeons can identify important features to prevent LHF and establish strategies by adjusting modifiable factors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study design<\/jats:title><jats:p>Retrospective cohort study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of evidence<\/jats:title><jats:p>3.<\/jats:p><\/jats:sec>","DOI":"10.1007\/s00167-022-07137-6","type":"journal-article","created":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T00:02:21Z","timestamp":1661731341000},"page":"3070-3078","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Development of a machine learning model to predict lateral hinge fractures by analyzing patient factors before open wedge high tibial osteotomy"],"prefix":"10.1002","volume":"31","author":[{"given":"Ho Won","family":"Jeong","sequence":"first","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopedic Surgery Seoul National University College of Medicine, Seoul National University Bundang Hospital 166 Gumi\u2010ro, Bundang\u2010gu 463\u2010707 Seongnam\u2010si Gyeonggi\u2010do South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Myeongju","family":"Kim","sequence":"additional","affiliation":[{"name":"<!--<label>2<\/label>--> Center for Artificial Intelligence in Healthcare Seoul National University Bundang Hospital Healthcare Innovation Park 166 Gumi\u2010ro, Bundang\u2010gu Seongnam\u2010si Gyeonggi\u2010do South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han Gyeol","family":"Choi","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopedic Surgery Seoul National University College of Medicine, Seoul National University Bundang Hospital 166 Gumi\u2010ro, Bundang\u2010gu 463\u2010707 Seongnam\u2010si Gyeonggi\u2010do South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seong Yun","family":"Park","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopedic Surgery Seoul National University College of Medicine, Seoul National University Bundang Hospital 166 Gumi\u2010ro, Bundang\u2010gu 463\u2010707 Seongnam\u2010si Gyeonggi\u2010do South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4641-305X","authenticated-orcid":false,"given":"Yong Seuk","family":"Lee","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopedic Surgery Seoul National University College of Medicine, Seoul National University Bundang Hospital 166 Gumi\u2010ro, Bundang\u2010gu 463\u2010707 Seongnam\u2010si Gyeonggi\u2010do South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,8,29]]},"reference":[{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1177\/03635465211028985"},{"key":"e_1_2_9_3_2","volume-title":"Deep learning with Python","author":"Francois C","year":"2017"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1080\/17453674.2021.1910448"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2019.01.026"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00167\u2010011\u20101706\u20107"},{"issue":"112","key":"e_1_2_9_7_2","article-title":"Can machine learning methods produce accurate and easy\u2010to\u2010use preoperative prediction models of one\u2010year improvements in pain and functioning after knee arthroplasty?","volume":"36","author":"Harris AH","year":"2021","journal-title":"J Arthroplasty"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/jbmr.4342"},{"key":"e_1_2_9_9_2","doi-asserted-by":"crossref","unstructured":"JeniLA CohnJF De La TorreF(2013)Facing imbalanced data\u2013recommendations for the use of performance metrics.Paper presented at: 2013 Humaine association conference on affective computing and intelligent interaction.","DOI":"10.1109\/ACII.2013.47"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arthro.2019.01.044"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arthro.2017.08.310"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1177\/23259671211035372"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arthro.2018.06.041"},{"key":"e_1_2_9_14_2","unstructured":"LundbergSM ErionGG LeeS\u2010I(2018)Consistent individualized feature attribution for tree ensembles.arXiv preprintarXiv:1802.03888"},{"key":"e_1_2_9_15_2","unstructured":"LundbergSM LeeS\u2010I(2017)A unified approach to interpreting model predictions.Advances in neural information processing systems 30 NIPS 2017 Long Beach CA USA"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00167\u2010010\u20101335\u20106"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1177\/0363546505275488"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arthro.2008.12.020"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1302\/0301\u2010620X.97B9.34949"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1177\/0363546520988021"},{"key":"e_1_2_9_21_2","doi-asserted-by":"crossref","unstructured":"RayS.A quick review of machine learning algorithms(2019) Paper presented at: 2019 International conference on machine learning big data cloud and parallel computing (COMITCon).","DOI":"10.1109\/COMITCon.2019.8862451"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1053\/j.oto.2006.09.014"},{"key":"e_1_2_9_23_2","first-page":"366","article-title":"Some studies in machine learning using the game of checkers. II\u2010recent progress","author":"Samuel AL","year":"1988","journal-title":"Computer Games"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinbiomech.2004.06.007"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.spinee.2021.02.024"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1177\/0363546507312646"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12891\u2010016\u20101277\u20103"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1093\/jpepsy\/jst062"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12967\u2010021\u201002990\u20104"},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1201\/b12207"}],"container-title":["Knee Surgery, Sports Traumatology, Arthroscopy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00167-022-07137-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00167-022-07137-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00167-022-07137-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/esskajournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1007\/s00167-022-07137-6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T14:18:51Z","timestamp":1760883531000},"score":1,"resource":{"primary":{"URL":"https:\/\/esskajournals.onlinelibrary.wiley.com\/doi\/10.1007\/s00167-022-07137-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,29]]},"references-count":29,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["10.1007\/s00167-022-07137-6"],"URL":"https:\/\/doi.org\/10.1007\/s00167-022-07137-6","archive":["Portico"],"relation":{},"ISSN":["0942-2056","1433-7347"],"issn-type":[{"value":"0942-2056","type":"print"},{"value":"1433-7347","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,29]]},"assertion":[{"value":"24 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}