{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:38:11Z","timestamp":1743129491960,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811675010"},{"type":"electronic","value":"9789811675027"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-7502-7_24","type":"book-chapter","created":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T09:06:55Z","timestamp":1635498415000},"page":"217-227","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Class Imbalance Monitoring Model for Fetal Heart Contractions Based on Gradient Boosting Decision Tree Ensemble Learning"],"prefix":"10.1007","author":[{"given":"Chen","family":"Qin","sequence":"first","affiliation":[]},{"given":"Shaopeng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Shengxiang","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Guangzhe","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jiaming","family":"Hong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"24_CR1","unstructured":"China Statistical Yearbook: China Statistical Publishing House, Beijing (2017)"},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"Alfirevic, Z., Devane, D., Gyte, G.M.L.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database Syst. Rev. 5(3), CD006066 (2006)","DOI":"10.1002\/14651858.CD006066"},{"key":"24_CR3","unstructured":"Li-Jun, W., Ming-Quan, C., An-Bo, L.: Study of continuous electronic heart rate monitoring during delivery period in rural district. Chin. J. Obstet. Gynecol. Pediatrics (2007)"},{"issue":"2","key":"24_CR4","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1111\/j.1479-828X.1993.tb02379.x","volume":"33","author":"MP Umstad","year":"2010","unstructured":"Umstad, M.P.: The predictive value of abnormal fetal heart rate patterns in early labour. Aust. N. Z. J. Obstet. Gynaecol. 33(2), 145\u2013149 (2010)","journal-title":"Aust. N. Z. J. Obstet. Gynaecol."},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Fergus, P., Hussain, A., Al-Jumeily, D., Huang, D.-S., Bouguila, N.: Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms.\u00a0Biomed. Eng. Online 16(1), 89 (2017)","DOI":"10.1186\/s12938-017-0378-z"},{"issue":"6","key":"24_CR6","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1007\/s00521-012-1110-3","volume":"23","author":"H Ocak","year":"2013","unstructured":"Ocak, H., Ertunc, H.M.: Prediction of fetal state from the cardiotocogram recordings using adaptive neuro -fuzzy inference s ystems. Neural Comput. Appl. 23(6), 1583\u20131589 (2013)","journal-title":"Neural Comput. Appl."},{"issue":"2","key":"24_CR7","doi-asserted-by":"publisher","first-page":"9913","DOI":"10.1007\/s10916-012-9913-4","volume":"37","author":"H Ocak","year":"2013","unstructured":"Ocak, H.: A medical decision support system based on support vector machines and the genetic algorithm for the evaluation of fetal well-being. J. Med. Syst. 37(2), 9913 (2013)","journal-title":"J. Med. Syst."},{"issue":"6","key":"24_CR8","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1007\/s40846-016-0191-3","volume":"36","author":"E Y\u0131lmaz","year":"2016","unstructured":"Y\u0131lmaz, E.: Fetal state assessment from cardiotocogram data using artificial neural networks. J. Med. Biol. Eng. 36(6), 820\u2013832 (2016)","journal-title":"J. Med. Biolo. Eng."},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Huang, M.L., Hsu, Y.Y.: Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network. J. Biomed. Sci. Eng. 05(9) (2012)","DOI":"10.4236\/jbise.2012.59065"},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Sundar, C., Chitradevi, M., Geetharamani G.: Classification of cardiotocogram data using neural network based machine learning technique. Int. J. Comput. Appl. 47(14), 19\u201325 (2013)","DOI":"10.5120\/7256-0279"},{"issue":"9","key":"24_CR11","doi-asserted-by":"publisher","first-page":"32","DOI":"10.4236\/jcc.2014.29005","volume":"02","author":"EM Karabulut","year":"2014","unstructured":"Karabulut, E.M., Ibrikci, T.: Analysis of cardiotocogram data for fetal distress determination by decision tree based adaptive boosting approach. J. Comput. Commun. 02(9), 32\u201337 (2014)","journal-title":"J. Comput. Commun."},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Arif, M.: Classification of cardiotocograms using random forest classifier and selection of important features from cardiotocogram signal. Biomater. Biomech. Bioeng. 2(3), 173\u2013183 (2015)","DOI":"10.12989\/bme.2015.2.3.173"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Y\u0131lmaz, E.: Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree. Comput. Math. Methods Med. 2013(2), 487179 (2013)","DOI":"10.1155\/2013\/487179"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Ravindran, S., Jambek, A.B., Muthusamy, H., et al.: A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being. Comput. Math. Methods Med. 2015 (2015)","DOI":"10.1155\/2015\/283532"},{"key":"24_CR15","doi-asserted-by":"crossref","unstructured":"Lessmann, S., Baesens, B., Scow, H.V., et al.: Benehmarking state-of-the-art classification algorithms for credit scoring: an update of research. Eur. J. Oper. Res. 247(1), 1(32) (2015)","DOI":"10.1016\/j.ejor.2015.05.030"},{"key":"24_CR16","doi-asserted-by":"crossref","unstructured":"Krebs, H.B., Petres, R.E.: Clinical application of a scoring system for evaluation of antepartum fetal heart rate monitoring. Am. J. Obstet. Gynecol. 130(7), 765\u201372 (1978)","DOI":"10.1016\/0002-9378(78)90006-6"},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Sahin, H., Subasi, A.: Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques. Appl. Soft Comput. 33(C), 231\u2013238 (2015)","DOI":"10.1016\/j.asoc.2015.04.038"},{"key":"24_CR18","doi-asserted-by":"crossref","unstructured":"Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced data sets. ACM SIGKDD Explor. News\u00b7Lett. 6(1), 1\u20136 (2004)","DOI":"10.1145\/1007730.1007733"}],"container-title":["Communications in Computer and Information Science","Data Mining and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-7502-7_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T09:12:12Z","timestamp":1635498732000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-7502-7_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811675010","9789811675027"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-7502-7_24","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DMBD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Mining and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dmbd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/dmbd2021\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"258","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"57","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}