[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125207-en":3,"doc-seo-125207-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125207,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data","Breast cancer remains a major global health concern with increasing incidence and mortality in many developing countries, making reliable risk assessment essential for prevention and early detection. This study compares AI-based machine learning classification approaches with the traditional Gail model in predicting breast cancer risk using a population dataset. Results from 942 newly diagnosed patients and 975 healthy controls evaluate accuracy, sensitivity, precision, and feature importance across ten algorithms, showing limited overall gains over Gail while highlighting substantial differences in variable importance and interactions.","University of Birmingham  \nComparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data  \nRafiepoor, Haniyeh; Ghorbankhanloo, Alireza; Zendehdel, Kazem; Madar, Zahra Zangeneh; Hajivalizadeh, Sepideh; Hasani, Zeinab; Sarmadi, Ali; Amanpour-Gharaei, Behzad; Barati,  \nMohammad Amin; Saadat, Mozafar; Sadegh-Zadeh, Seyed Ali; Amanpour, Saeid DOI:  \n10.1002/cnr2.70175  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nPublisher's PDF, also known as Version of record Citation for published version (Harvard):  \nRafiepoor, H, Ghorbankhanloo, A, Zendehdel, K, Madar, ZZ, Hajivalizadeh, S, Hasani, Z, Sarmadi, A, Amanpour-Gharaei, B, Barati, MA, Saadat, M, Sadegh-Zadeh, SA & Amanpour, S 2025, 'Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data', Cancer Reports, vol.  \n8, no. 4, e70175 . [https://doi.org/10.1002/cnr2.70175](https://doi.org/10.1002/cnr2.70175)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.  \n•Users may freely distribute the URL that is used to identify this publication.  \n•Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.  \n•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)  \n•Users may not further distribute the material nor use it for the purposes of commercial gain.  \nWhere a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.  \nWhen citing, please reference the published version.  \nTake down policy  \nWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.  \nIf you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate.  \nDownload date: 04. Aug. 2026  \nCancer Reports  \nORIGINAL ARTICLE  OPEN ACCESS   \nComparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data  \nHaniyeh Rafiepoor1 | Alireza Ghorbankhanloo1 | Kazem Zendehdel1 | Zahra Zangeneh Madar2,3 | Sepideh Hajivalizadeh4 | Zeinab Hasani5 | Ali Sarmadi6 | Behzad Amanpour-Gharaei1 | Mohammad Amin Barati7 | Mozafar Saadat8 |  \nSeyed-Ali Sadegh-Zadeh9 | Saeid Amanpour1   \n1Cancer Biology Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran | 2School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran | 3Department of Industrial Engineering, Iran University of Science and Technology, Tehran,  \nIran | 4Osteoporosis Research Center, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran | 5School of Medicine, Tehran University of Medical Science, Tehran, Iran | 6Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran | 7School of Mechanical Engineering, University of Tehran, Tehran, Iran | 8Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham, UK | 9Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-onTrent, UK  \nCorrespondence: Seyed-Ali Sadegh-Zadeh ([ali.sadegh-zadeh@staffs.ac.uk](ali.sadegh-zadeh@staffs.ac.uk)) | Saeid Amanpour ([amanpour_s@tums.ac.ir](amanpour_s@tums.ac.ir))  \nReceived: 26 Apri","cbCaiqNJzhXw9aNJ","https://ap.wps.com/l/cbCaiqNJzhXw9aNJ","pdf",504343,1,10,"English","en",105,"# ABSTRACT\n## Background\n## Aims\n## Methods and Results\n## Conclusion","[{\"question\":\"What is the main purpose of this study?\",\"answer\":\"To compare the predictive quality of AI-based machine learning models with the traditional Gail model for breast cancer risk assessment using a population dataset.\"},{\"question\":\"How was the study conducted and what data were used?\",\"answer\":\"The study used 942 newly diagnosed breast cancer patients and 975 healthy controls from the Cancer Institute in the IKH hospital complex in Tehran, applying ten classification algorithms.\"},{\"question\":\"What were the key findings about model performance and feature importance?\",\"answer\":\"AI algorithms did not significantly improve predictability compared with the Gail model, but the importance of variables differed markedly among AI algorithms, emphasizing the need to understand feature importance and interactions.\"}]","Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data | 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is the main purpose of this study?","Question",{"text":75,"@type":76},"To compare the predictive quality of AI-based machine learning models with the traditional Gail model for breast cancer risk assessment using a population dataset.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the study conducted and what data were used?",{"text":80,"@type":76},"The study used 942 newly diagnosed breast cancer patients and 975 healthy controls from the Cancer Institute in the IKH hospital complex in Tehran, applying ten classification algorithms.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the key findings about model performance and feature importance?",{"text":84,"@type":76},"AI algorithms did not significantly improve predictability compared with the Gail model, but the importance of variables differed markedly among AI algorithms, emphasizing the need to understand feature importance and 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