{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T12:44:03Z","timestamp":1774529043913,"version":"3.50.1"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2019,8,5]],"date-time":"2019-08-05T00:00:00Z","timestamp":1564963200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,8,5]],"date-time":"2019-08-05T00:00:00Z","timestamp":1564963200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s00521-019-04378-4","type":"journal-article","created":{"date-parts":[[2019,8,5]],"date-time":"2019-08-05T12:02:31Z","timestamp":1565006551000},"page":"1971-1979","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":103,"title":["Research on orthopedic auxiliary classification and prediction model based on XGBoost algorithm"],"prefix":"10.1007","volume":"32","author":[{"given":"Shenglong","family":"Li","sequence":"first","affiliation":[]},{"given":"Xiaojing","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,5]]},"reference":[{"issue":"59","key":"4378_CR1","first-page":"39","volume":"16","author":"H Zhu","year":"2016","unstructured":"Zhu H (2016) Comparative analysis of postoperative complications between minimally invasive surgery and traditional surgery in orthopedic patients. World Med Inf Digest 16(59):39","journal-title":"World Med Inf Digest"},{"issue":"5","key":"4378_CR2","first-page":"5","volume":"32","author":"H Wang","year":"2018","unstructured":"Wang H, Tian X, You M et al (2018) Application status, problems and suggestions of artificial intelligence in medical field. J Hyg Soft Sci 32(5):5\u20137","journal-title":"J Hyg Soft Sci"},{"key":"4378_CR3","first-page":"77","volume":"05","author":"X Zhang","year":"2017","unstructured":"Zhang X, Chen L, Zhao M (2017) Application of big data mining and analysis in health care. Air Force Med J 05:77\u201379","journal-title":"Air Force Med J"},{"issue":"Suppl 1","key":"4378_CR4","first-page":"S62","volume":"33","author":"G Rossi","year":"2018","unstructured":"Rossi G, Association AD (2018) Diagnosis and classification of diabetes mellitus. Diabetes Care 33(Suppl 1):S62\u2013S69","journal-title":"Diabetes Care"},{"key":"4378_CR5","doi-asserted-by":"crossref","unstructured":"Abe H et al (2007) Developing an intefrated time-series data mining environment for medical data mining. In: Seventh IEEE international conference on data mining workshops, 2007. ICDM Workshops 2007, IEEE","DOI":"10.1109\/ICDMW.2007.47"},{"key":"4378_CR6","doi-asserted-by":"crossref","unstructured":"Kasemthaweesab P, Kurutach W (2012) Association analysis of diabetes mellitus (DM) with complication states based on association rules. In: Industrial electronics & applications, IEEE","DOI":"10.1109\/ICIEA.2012.6360952"},{"issue":"2","key":"4378_CR7","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1109\/TBME.2016.2573285","volume":"64","author":"PY Wu","year":"2017","unstructured":"Wu PY, Cheng CW, Kaddi CD et al (2017) Omic and electronic health record big data analytics for precision medicine. IEEE Trans Biomed Eng 64(2):263\u2013273","journal-title":"IEEE Trans Biomed Eng"},{"key":"4378_CR8","doi-asserted-by":"crossref","unstructured":"Kaushik S, Choudhury A, Dasgupta N et al (2018) Evaluating frequent-set mining approaches in machine-learning problems with several attributes: a case study in healthcare. In: International conference on machine learning and data mining in pattern recognition. Springer, Cham, pp 244\u2013258","DOI":"10.1007\/978-3-319-96136-1_20"},{"issue":"4","key":"4378_CR9","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1166\/jmihi.2016.1807","volume":"6","author":"J Li","year":"2016","unstructured":"Li J, Fong S, Mohammed S et al (2016) Solving the under-fitting problem for decision tree algorithms by incremental swarm optimization in rare-event healthcare classification. J Med Imaging Health Inform 6(4):1102\u20131110","journal-title":"J Med Imaging Health Inform"},{"issue":"6","key":"4378_CR10","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1038\/nrcardio.2016.42","volume":"13","author":"JS Rumsfeld","year":"2016","unstructured":"Rumsfeld JS, Joynt KE, Maddox TM (2016) Big data analytics to improve cardiovascular care: promise and challenges. Nat Rev Cardiol 13(6):350","journal-title":"Nat Rev Cardiol"},{"issue":"3","key":"4378_CR11","first-page":"38","volume":"13","author":"X Kaijian","year":"2018","unstructured":"Kaijian X, Jianqiang W, Yong Y (2018) Medical data classification and early prediction of kidney disease based on WEKA data mining platform. China Digit Med 13(3):38\u201340","journal-title":"China Digit Med"},{"key":"4378_CR12","first-page":"276","volume":"1","author":"X Jin","year":"2018","unstructured":"Jin X, Ge G, Lu X et al (2018) Threshold optimization algorithm for unbalanced data classification and prediction \u03b5-KSVM. Comput Appl Softw 1:276\u2013280","journal-title":"Comput Appl Softw"},{"issue":"1","key":"4378_CR13","first-page":"139","volume":"33","author":"M Gao","year":"2016","unstructured":"Gao M, Tang S, Xu F (2016) Application research of X-11-ARIMA prediction model in hospital data mining platform. Chin J Health Stat 33(1):139\u2013141","journal-title":"Chin J Health Stat"},{"issue":"19","key":"4378_CR14","first-page":"20","volume":"36","author":"Q Liu","year":"2017","unstructured":"Liu Q, Chen S, Huang L (2017) Regular prediction of data mining in fetal heart rate. Microcomput Appl 36(19):20\u201322","journal-title":"Microcomput Appl"},{"issue":"4","key":"4378_CR15","first-page":"64","volume":"11","author":"J Xu","year":"2016","unstructured":"Xu J, Yan R (2016) Application of a fast feature selection method in medical data mining. Chin J Digit Medicine 11(4):64\u201366","journal-title":"Chin J Digit Medicine"},{"key":"4378_CR16","first-page":"41","volume":"6","author":"Z Lin","year":"2016","unstructured":"Lin Z (2016) Online medical diagnosis system based on data mining. Comput Inf Technol 6:41\u201343","journal-title":"Comput Inf Technol"},{"issue":"2","key":"4378_CR17","first-page":"51","volume":"39","author":"W Wenming","year":"2018","unstructured":"Wenming W, Yun L, Wei Z, Hongwei S, Xianghua Y, Zhongzheng D (2018) Research on classification and tiered storage technology of hospital health information data. Med Equip 39(2):51\u201355","journal-title":"Med Equip"},{"key":"4378_CR18","first-page":"108","volume":"02","author":"W Zhang","year":"2017","unstructured":"Zhang W, Ji H, Zhang H (2017) Application of XGBoost algorithm in e-commerce product recommendation. Internet Things Technol 02:108\u2013110","journal-title":"Internet Things Technol"},{"key":"4378_CR19","first-page":"37","volume":"2","author":"D You","year":"2018","unstructured":"You D, Mo Z (2018) Research on bank credit evaluation based on fuzzy xgboost algorithm. Inf Commun 2:37\u201338","journal-title":"Inf Commun"},{"issue":"4","key":"4378_CR20","first-page":"10","volume":"1","author":"D Huang","year":"2017","unstructured":"Huang D, Fang W (2017) Practical application of electricity consumption prediction based on XGBoost algorithm. Mod Inf Technol 1(4):10\u201312","journal-title":"Mod Inf Technol"},{"issue":"07","key":"4378_CR21","first-page":"4","volume":"39","author":"Y Xu","year":"2018","unstructured":"Xu Y, Yang J, Li N et al (2018) Application of Xgboost algorithm in regional power forecasting. Autom Instrum 39(07):4\u20138","journal-title":"Autom Instrum"},{"issue":"05","key":"4378_CR22","first-page":"60","volume":"15","author":"K Lin","year":"2018","unstructured":"Lin K, Lin Y, Kong G (2018) Prediction of hospitalized death risk in patients with sepsis in ICU based on XGBoost algorithm. China J Health Inf Manag 15(05):60\u201364","journal-title":"China J Health Inf Manag"},{"key":"4378_CR23","first-page":"61","volume":"6","author":"J Jiang","year":"2017","unstructured":"Jiang J, Liu W (2017) Application of XGBoost algorithm in manufacturing quality prediction. Intell Comput Appl 6:61\u201363","journal-title":"Intell Comput Appl"},{"issue":"09","key":"4378_CR24","first-page":"108","volume":"213","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Yao Y (2018) Research on network intrusion detection based on Xgboost algorithm. Inf Netw Secur 213(09):108\u2013111","journal-title":"Inf Netw Secur"},{"issue":"3","key":"4378_CR25","first-page":"69","volume":"13","author":"Y Gao","year":"2018","unstructured":"Gao Y (2018) Case classification system design based on XGBoost algorithm. China Digit Med 13(3):69\u201371","journal-title":"China Digit Med"},{"key":"4378_CR26","first-page":"62","volume":"23","author":"C Wang","year":"2017","unstructured":"Wang C, Han D (2017) Research on internet customer churn prediction based on social network analysis and XGBoost algorithm. Microcomput Appl 23:62\u201365","journal-title":"Microcomput Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04378-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04378-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04378-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,3]],"date-time":"2020-08-03T23:24:22Z","timestamp":1596497062000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04378-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,5]]},"references-count":26,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["4378"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04378-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,5]]},"assertion":[{"value":"2 April 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"There are no conflicts of interest of this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}