{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:50:30Z","timestamp":1771703430908,"version":"3.50.1"},"reference-count":56,"publisher":"Elsevier BV","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.elsevier.com\/tdm\/userlicense\/1.0\/"},{"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.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T00:00:00Z","timestamp":1620604800000},"content-version":"vor","delay-in-days":129,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Cancer Treatment and Research Communications"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1016\/j.ctarc.2021.100396","type":"journal-article","created":{"date-parts":[[2021,5,15]],"date-time":"2021-05-15T11:46:17Z","timestamp":1621079177000},"page":"100396","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":33,"special_numbering":"C","title":["Breast cancer risk prediction in African women using Random Forest Classifier"],"prefix":"10.1016","volume":"28","author":[{"given":"Babafemi Oluropo","family":"Macaulay","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin Segun","family":"Aribisala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soji Alabi","family":"Akande","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boluwaji Ade","family":"Akinnuwesi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olusola Aanu","family":"Olabanjo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.ctarc.2021.100396_bib0001","first-page":"188","article-title":"Data mining classification techniques applied for breast cancer diagnosis and prognosis","volume":"2","author":"Gupta","year":"2011","journal-title":"Indian J Comput. Sci. Eng."},{"key":"10.1016\/j.ctarc.2021.100396_bib0002","series-title":"The Biology of Cancer","author":"Weinberg","year":"2013"},{"issue":"76","key":"10.1016\/j.ctarc.2021.100396_bib0003","first-page":"1","article-title":"Computational systems biology of cancer","volume":"12","author":"Barillot","year":"2012","journal-title":"Biomed Eng Online"},{"key":"10.1016\/j.ctarc.2021.100396_bib0004","series-title":"Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics","author":"Pecorino","year":"2012"},{"issue":"6","key":"10.1016\/j.ctarc.2021.100396_bib0005","first-page":"394","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA Cancer J Clin"},{"issue":"10","key":"10.1016\/j.ctarc.2021.100396_bib0006","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1002\/cncr.20276","article-title":"Patterns and predictors of colorectal cancer test use in the adult US population","volume":"100","author":"Seeff","year":"2004","journal-title":"Cancer: Interdiscip Int.l J. Am. Cancer Soc."},{"issue":"2","key":"10.1016\/j.ctarc.2021.100396_bib0007","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s12013-014-0459-6","article-title":"Breast cancer: epidemiology and etiology","volume":"72","author":"Tao","year":"2015","journal-title":"Cell Biochem. Biophys."},{"issue":"2","key":"10.1016\/j.ctarc.2021.100396_bib0008","article-title":"Breast cancer risk prediction using data mining classification techniques","volume":"3","author":"Williams","year":"2015","journal-title":"Trans. Netw. Commun."},{"key":"10.1016\/j.ctarc.2021.100396_bib0009","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.jbi.2014.01.010","article-title":"Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach","volume":"49","author":"Dheeba","year":"2014","journal-title":"J Biomed Inform"},{"issue":"2","key":"10.1016\/j.ctarc.2021.100396_bib0010","first-page":"2456","article-title":"A novel approach for breast cancer detection using data mining techniques","volume":"2017","author":"Chaurasia","year":"2014","journal-title":"Int. J. Innovative Res. Comput. Commun. Eng."},{"issue":"6","key":"10.1016\/j.ctarc.2021.100396_bib0011","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/s10552-020-01272-6","article-title":"Validation of two US breast cancer risk prediction models in German women","volume":"31","author":"H\u00fcsing","year":"2020","journal-title":"Cancer Causes & Control"},{"issue":"1","key":"10.1016\/j.ctarc.2021.100396_bib0012","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-018-06616-0","article-title":"Characterization of Nigerian breast cancer reveals prevalent homologous recombination deficiency and aggressive molecular features","volume":"9","author":"Pitt","year":"2018","journal-title":"Nat Commun"},{"key":"10.1016\/j.ctarc.2021.100396_bib0013","doi-asserted-by":"crossref","unstructured":"Kumar, N., V. Singh, and G. Mehta, Assessment of Common Risk Factors and Validation of the Gail Model for Breast Cancer: A hospital-Based Study from Western India.2020.","DOI":"10.4103\/tcmj.tcmj_171_19"},{"issue":"6","key":"10.1016\/j.ctarc.2021.100396_bib0014","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1158\/1055-9965.EPI-17-1128","article-title":"Development of a breast cancer risk prediction model for women in Nigeria","volume":"27","author":"Wang","year":"2018","journal-title":"Cancer Epidemiol. Prev. Biomarkers"},{"issue":"3","key":"10.1016\/j.ctarc.2021.100396_bib0015","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1007\/s10549-014-2868-z","article-title":"Breast cancer in Sub-Saharan Africa: opportunities for prevention","volume":"144","author":"Brinton","year":"2014","journal-title":"Breast Cancer Res. Treat."},{"issue":"5","key":"10.1016\/j.ctarc.2021.100396_bib0016","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1093\/jnci\/djt008","article-title":"Validation of a breast cancer risk prediction model developed for Black women","volume":"105","author":"Boggs","year":"2013","journal-title":"J. Natl. Cancer Inst."},{"issue":"3","key":"10.1016\/j.ctarc.2021.100396_bib0017","first-page":"179","article-title":"Breast cancer in Nigeria","volume":"19","author":"Adebamowo","year":"2000","journal-title":"West Afr J Med"},{"key":"10.1016\/j.ctarc.2021.100396_bib0018","series-title":"Introduction to Machine Learning","author":"Alpaydin","year":"2020"},{"key":"10.1016\/j.ctarc.2021.100396_bib0019","series-title":"Deep Learning in Medical Image analysis: Challenges and Applications","author":"Lee","year":"2020"},{"issue":"104","key":"10.1016\/j.ctarc.2021.100396_bib0020","first-page":"1","article-title":"Prediction of breast cancer by profiling of urinary RNA metabolites using Support Vector Machine-based feature selection","volume":"9","author":"Carsten Henneges","year":"2009","journal-title":"Biomed. Centre"},{"key":"10.1016\/j.ctarc.2021.100396_bib0021","series-title":"Breast Cancer Prediction and Cross Validation Using Multilayer Perceptron Neural Networks","author":"Mojarad","year":"2010"},{"key":"10.1016\/j.ctarc.2021.100396_bib0022","doi-asserted-by":"crossref","first-page":"2901","DOI":"10.1007\/s10916-011-9768-0","article-title":"Prediction of breast cancer using artificial neural networks","volume":"36","author":"Saritas","year":"2012","journal-title":"J. Med Syst."},{"key":"10.1016\/j.ctarc.2021.100396_bib0023","series-title":"Probabilistic Neural Network for Breast Biopsy Classification","first-page":"101","author":"Al-Timemy","year":"2009"},{"key":"10.1016\/j.ctarc.2021.100396_bib0024","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10549-011-1853-z","article-title":"Risk prediction models of breast cancer: a systematic review of model performances","volume":"133","author":"Anothaisintawee","year":"2012","journal-title":"Breast Cancer Res. Treat."},{"key":"10.1016\/j.ctarc.2021.100396_bib0025","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1016\/j.compbiomed.2007.02.003","article-title":"Diagnosis of breast cancer using Bayesian networks:acase study","volume":"37","author":"Cruz-Ram\u00edreza","year":"2007","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ctarc.2021.100396_bib0026","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.ejor.2006.06.031","article-title":"Breast cancer prediction using the isotonic separation technique","volume":"181","author":"Ryu","year":"2007","journal-title":"Eur J Oper Res"},{"key":"10.1016\/j.ctarc.2021.100396_bib0027","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1007\/978-3-642-20320-6_65","article-title":"Application of pattern recognition techniques for the analysis of histopathological images","volume":"4","author":"Adam Krzy\u02d9zak","year":"2011","journal-title":"Comput Recognit Syst."},{"issue":"2","key":"10.1016\/j.ctarc.2021.100396_bib0028","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.eij.2010.10.005","article-title":"Abdel Moniem c, Support vector machine for diagnosis cancer disease: a comparative study","volume":"11","author":"Nasser","year":"2010","journal-title":"Egypt Inform. J."},{"issue":"5","key":"10.1016\/j.ctarc.2021.100396_bib0029","first-page":"756","article-title":"Analysis of feature selection with classification: breast cancer datasets","volume":"2","author":"Lavanya","year":"2011","journal-title":"Indian J. Comput. Sci. Eng."},{"issue":"569","key":"10.1016\/j.ctarc.2021.100396_bib0030","first-page":"2","article-title":"Breast cancer diagnosis on three different datasets using multi-classifiers","volume":"32","author":"Salama","year":"2012","journal-title":"Breast Cancer (WDBC)"},{"issue":"1","key":"10.1016\/j.ctarc.2021.100396_bib0031","first-page":"17","article-title":"Ensemble decision tree classifier for breast cancer data","volume":"2","author":"Lavanya","year":"2012","journal-title":"Int. J. Inform. Technol. Convergence Serv."},{"key":"10.1016\/j.ctarc.2021.100396_bib0032","doi-asserted-by":"crossref","unstructured":"Kharya, S., Using Data Mining Techniques For Diagnosis and Prognosis of Cancer Disease.arXiv preprint arXiv:1205.1923, 2012.","DOI":"10.5121\/ijcseit.2012.2206"},{"key":"10.1016\/j.ctarc.2021.100396_bib0033","series-title":"2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC","article-title":"Improving the prediction accuracy of decision tree mining with data preprocessing","author":"Chandrasekar","year":"2017"},{"key":"10.1016\/j.ctarc.2021.100396_bib0034","series-title":"International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems","article-title":"Multi-modal biometric emotion recognition using classifier ensembles","author":"Kuncheva","year":"2011"},{"issue":"1","key":"10.1016\/j.ctarc.2021.100396_bib0035","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/1471-2105-7-3","article-title":"Gene selection and classification of microarray data using random forest","volume":"7","author":"D\u00edaz-Uriarte","year":"2006","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"10.1016\/j.ctarc.2021.100396_bib0036","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1186\/1472-6947-11-51","article-title":"Predicting disease risks from highly imbalanced data using random forest","volume":"11","author":"Khalilia","year":"2011","journal-title":". BMC Med. Inform. Decis. Making"},{"key":"10.1016\/j.ctarc.2021.100396_bib0037","series-title":"19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007)","article-title":"An empirical study of learning from imbalanced data using random forest","author":"Khoshgoftaar","year":"2007"},{"key":"10.1016\/j.ctarc.2021.100396_bib0038","series-title":"Modeling Species Distribution and Change Using Random Forest, in Predictive species and Habitat Modeling in Landscape Ecology","first-page":"139","author":"Evans","year":"2011"},{"key":"10.1016\/j.ctarc.2021.100396_bib0039","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.cmpb.2016.03.020","article-title":"Congestive heart failure detection using random forest classifier","volume":"130","author":"Masetic","year":"2016","journal-title":"Comput Methods Programs Biomed"},{"issue":"16","key":"10.1016\/j.ctarc.2021.100396_bib0040","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/s12665-019-8518-3","article-title":"Land subsidence susceptibility assessment using random forest machine learning algorithm","volume":"78","author":"Mohammady","year":"2019","journal-title":"Environ Earth Sci"},{"key":"10.1016\/j.ctarc.2021.100396_bib0041","series-title":"CMBEBIH 2017","first-page":"589","article-title":"Diagnosis of chronic kidney disease by using random forest","author":"Subasi","year":"2017"},{"issue":"114","key":"10.1016\/j.ctarc.2021.100396_bib0042","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: a review of applications and future directions","author":"Belgiu a","year":"2016","journal-title":"ISPRS J. Photogrammetry Remote Sensing"},{"key":"10.1016\/j.ctarc.2021.100396_bib0043","series-title":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence","article-title":"AdaBoost algorithm with random forests for predicting breast cancer survivability","author":"Thongkam","year":"2008"},{"key":"10.1016\/j.ctarc.2021.100396_bib0044","series-title":"Proceedings on Privacy Enhancing Technologies","first-page":"335","article-title":"Privately evaluating decision trees and random forests","volume":"2016","author":"Wu","year":"2016"},{"issue":"5","key":"10.1016\/j.ctarc.2021.100396_bib0045","first-page":"272","article-title":"Random forests and decision trees","volume":"9","author":"Ali","year":"2012","journal-title":"Int. J. Comput. Sci Issues"},{"issue":"2","key":"10.1016\/j.ctarc.2021.100396_bib0046","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.jksuci.2018.05.010","article-title":"Feature selection using an improved Chi-square for Arabic text classification","volume":"32","author":"Bahassine","year":"2020","journal-title":"J. King Saud University-Comput Inform Sci."},{"key":"10.1016\/j.ctarc.2021.100396_bib0047","series-title":"2019 International Conference on Information and Communications Technology (ICOIACT)","article-title":"The best features selection method and relevance variable for web phishing classification","author":"Adi","year":"2019"},{"issue":"1","key":"10.1016\/j.ctarc.2021.100396_bib0048","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1177\/0165551518770967","article-title":"Mutual information and sensitivity analysis for feature selection in customer targeting: a comparative study","volume":"45","author":"Barraza","year":"2019","journal-title":"J. Inform. Sci."},{"key":"10.1016\/j.ctarc.2021.100396_bib0049","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.patrec.2018.05.005","article-title":"A multi-feature selection approach for gender identification of handwriting based on kernel mutual information","volume":"121","author":"Bi","year":"2019","journal-title":"Pattern Recognit Lett"},{"key":"10.1016\/j.ctarc.2021.100396_bib0050","first-page":"1","article-title":"The application of gail model to predict the risk of developing breast cancer among jordanian women","volume":"2020","author":"Abdel-Razeq","year":"2020","journal-title":"J Oncol"},{"issue":"18","key":"10.1016\/j.ctarc.2021.100396_bib0051","first-page":"1","article-title":"Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis","volume":"20","author":"Wang","year":"2018","journal-title":"Breast Cancer Research"},{"key":"10.1016\/j.ctarc.2021.100396_bib0052","first-page":"1","article-title":"Breast cancer risk assessment by Gail Model in women of Baghdad","author":"Ewaid","year":"2016","journal-title":"Alexandria J. Med."},{"key":"10.1016\/j.ctarc.2021.100396_bib0053","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1111\/apa.13102","article-title":"Breastfeeding and maternal health outcomes: a systematic review and meta-analysis","volume":"104","author":"Chowdhury","year":"2015","journal-title":"Acta P\u00e6diatrica"},{"issue":"5","key":"10.1016\/j.ctarc.2021.100396_bib0054","article-title":"The impact of lifestyle and reproductive factors on the risk of a second new primary cancer in the contralateral breast: a systematic review and meta\u2011analysis","volume":"35","author":"Akdeniz","year":"2020","journal-title":"Springer J."},{"key":"10.1016\/j.ctarc.2021.100396_bib0055","first-page":"515","article-title":"A large-scale candidate gene association study of age at menarche and age at natural menopause","volume":"128","author":"He","year":"2010","journal-title":"Springer J"},{"key":"10.1016\/j.ctarc.2021.100396_bib0056","first-page":"763","article-title":"Employment after a breast cancer diagnosis: a qualitative study of ethnically diverse urban women","volume":"37","author":"Blinder","year":"2012","journal-title":"Springer J."}],"container-title":["Cancer Treatment and Research Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2468294221000940?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2468294221000940?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T06:23:52Z","timestamp":1762842232000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2468294221000940"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":56,"alternative-id":["S2468294221000940"],"URL":"https:\/\/doi.org\/10.1016\/j.ctarc.2021.100396","relation":{},"ISSN":["2468-2942"],"issn-type":[{"value":"2468-2942","type":"print"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Breast cancer risk prediction in African women using Random Forest Classifier","name":"articletitle","label":"Article Title"},{"value":"Cancer Treatment and Research Communications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ctarc.2021.100396","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"100396"}}