{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:46:16Z","timestamp":1778168776900,"version":"3.51.4"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T00:00:00Z","timestamp":1617062400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T00:00:00Z","timestamp":1617062400000},"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":["SN COMPUT. SCI."],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s42979-021-00551-6","type":"journal-article","created":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T16:02:52Z","timestamp":1617120172000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques"],"prefix":"10.1007","volume":"2","author":[{"given":"Laboni","family":"Akter","sequence":"first","affiliation":[]},{"family":"Ferdib-Al-Islam","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4535-5978","authenticated-orcid":false,"given":"Md. Milon","family":"Islam","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5343-8370","authenticated-orcid":false,"given":"Mabrook S.","family":"Al-Rakhami","sequence":"additional","affiliation":[]},{"given":"Md. Rezwanul","family":"Haque","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"issue":"12","key":"551_CR1","doi-asserted-by":"publisher","first-page":"2893","DOI":"10.1002\/ijc.25516","volume":"127","author":"J Ferlay","year":"2010","unstructured":"Ferlay J, Shin H, Bray F, Forman D, Mathers C, Parkin D. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127(12):2893\u2013917.","journal-title":"Int J Cancer"},{"key":"551_CR2","unstructured":"Guidelines for cervical cancer screening programme. Chandigarh: Department of Cytology & Gynaecological Pathology, Postgraduate Institute of Medical Education, Research, screening.iarc.fr, 2020. Accessed 29 Oct 2020."},{"key":"551_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1742-4755-9-11","volume":"9","author":"C Ndikom","year":"2012","unstructured":"Ndikom C, Ofi B. Awareness, perception and factors affecting utilization of cervical cancer screening services among women in Ibadan, Nigeria: a qualitative study. Reprod Health. 2012;9:1\u20138.","journal-title":"Reprod Health"},{"issue":"12","key":"551_CR4","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1093\/jjco\/hyt140","volume":"43","author":"S Hussain","year":"2013","unstructured":"Hussain S, Sullivan R. Cancer control in Bangladesh. Jpn J Clin Oncol. 2013;43(12):1159\u201369.","journal-title":"Jpn J Clin Oncol"},{"issue":"1","key":"551_CR5","first-page":"36","volume":"7","author":"BS Paul","year":"2011","unstructured":"Paul BS. Studies on the epidemiology of cervical cancer in Southern Assam. Assam Univ J Sci Technol. 2011;7(1):36\u201342.","journal-title":"Assam Univ J Sci Technol"},{"key":"551_CR6","doi-asserted-by":"crossref","unstructured":"Deng X, Luo Y., Wang C. Analysis of risk factors for cervical cancer based on machine learning methods. In: Proc. of 5th IEEE international conference on cloud computing and intelligence systems (CCIS), Nanjing, China, 2018. p. 631\u20135.","DOI":"10.1109\/CCIS.2018.8691126"},{"key":"551_CR7","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.future.2019.12.033","volume":"106","author":"J Lu","year":"2020","unstructured":"Lu J, Song E, Ghoneim A, Alrashoud M. Machine learning for assisting cervical cancer diagnosis: an ensemble approach. Futur Gener Comput Syst. 2020;106:199\u2013205.","journal-title":"Futur Gener Comput Syst"},{"issue":"6","key":"551_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42452-019-0645-7","volume":"1","author":"B Nithya","year":"2019","unstructured":"Nithya B, Ilango V. Evaluation of machine learning based optimized feature selection approaches and classification methods for cervical cancer prediction. SN Appl Sci. 2019;1(6):1\u201316.","journal-title":"SN Appl Sci"},{"issue":"1","key":"551_CR9","first-page":"53","volume":"5","author":"D Parikh","year":"2019","unstructured":"Parikh D, Menon V. Machine learning applied to cervical cancer data. Int J Math Sci Comput. 2019;5(1):53\u201364.","journal-title":"Int J Math Sci Comput"},{"issue":"6","key":"551_CR10","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1007\/s00521-013-1359-1","volume":"24","author":"C Tseng","year":"2013","unstructured":"Tseng C, Lu C, Chang C, Chen G. Application of machine learning to predict the recurrence-proneness for cervical cancer. Neural Comput Appl. 2013;24(6):1311\u20136.","journal-title":"Neural Comput Appl"},{"issue":"4","key":"551_CR11","first-page":"689","volume":"22","author":"S Suman","year":"2019","unstructured":"Suman S, Hooda N. Predicting risk of cervical cancer: a case study of machine learning. J Stat Manag Syst. 2019;22(4):689\u201396.","journal-title":"J Stat Manag Syst"},{"key":"551_CR12","unstructured":"UCI machine learning repository: cervical cancer behavior risk data set. Archive.ics.uci.edu, 2020. Accessed 10 Nov 2020."},{"issue":"10","key":"551_CR13","doi-asserted-by":"publisher","first-page":"3120","DOI":"10.1166\/asl.2016.7980","volume":"22","author":"R Machmud","year":"2016","unstructured":"Machmud R, Wijaya A. Behavior determinant based cervical cancer early detection with machine learning algorithm. Adv Sci Lett. 2016;22(10):3120\u20133.","journal-title":"Adv Sci Lett"},{"key":"551_CR14","doi-asserted-by":"crossref","unstructured":"Patro S, Sahu K. Normalization: a preprocessing stage. IARJSET. 2015. p. 20\u201322.","DOI":"10.17148\/IARJSET.2015.2305"},{"key":"551_CR15","doi-asserted-by":"crossref","unstructured":"Cox V. Translating statistics to make decisions. 2017.","DOI":"10.1007\/978-1-4842-2256-0"},{"issue":"12","key":"551_CR16","doi-asserted-by":"publisher","first-page":"2907","DOI":"10.3390\/ijerph15122907","volume":"15","author":"S Kumar","year":"2018","unstructured":"Kumar S, Chong I. Correlation analysis to identify the effective data in machine learning: prediction of depressive disorder and emotion states. Int J Environ Res Public Health. 2018;15(12):2907.","journal-title":"Int J Environ Res Public Health"},{"key":"551_CR17","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-3-030-45183-7","volume-title":"Smart applications and data analysis","author":"M Hamlich","year":"2020","unstructured":"Hamlich M, Bellatreche L, Mondal A, Ordonez C. Smart applications and data analysis. Cham: Springer; 2020. p. 165\u201377."},{"key":"551_CR18","doi-asserted-by":"publisher","first-page":"59475","DOI":"10.1109\/ACCESS.2018.2874063","volume":"6","author":"SF Abdoh","year":"2018","unstructured":"Abdoh SF, Abo Rizka M, Maghraby FA. Cervical cancer diagnosis using random forest classifier with SMOTE and feature reduction techniques. IEEE Access. 2018;6:59475\u201385.","journal-title":"IEEE Access"},{"key":"551_CR19","doi-asserted-by":"crossref","unstructured":"Dimitrakopoulos GN, Vrahatis AG, Plagianakos V, Sgarbas K. Pathway analysis using XGBoost classification in Biomedical Data. In: Proc. of the 10th hellenic conference on artificial intelligence. Association for computing machinery, New York, NY, USA, Article 46, 2018. p. 1\u20136.","DOI":"10.1145\/3200947.3201029"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-021-00551-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-021-00551-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-021-00551-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T17:36:22Z","timestamp":1620927382000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-021-00551-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,30]]},"references-count":19,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["551"],"URL":"https:\/\/doi.org\/10.1007\/s42979-021-00551-6","relation":{},"ISSN":["2662-995X","2661-8907"],"issn-type":[{"value":"2662-995X","type":"print"},{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,30]]},"assertion":[{"value":"22 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"177"}}