[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125495-en":3,"doc-seo-125495-105":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},125495,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data - Cancer Reports","Breast cancer (BC) remains a major global health challenge, with incidence and mortality rising in many developing countries. Accurate risk assessment supports prevention and early detection, yet the widely used Gail model may be constrained by its linear assumptions. This study compares ten AI-based classification approaches with the traditional Gail model on a clinical population dataset, evaluating predictive performance and feature importance.","Cancer 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 April 2024 | Revised: 1 February 2025 | Accepted: 20 February 2025  \nFunding: The authors received no specific funding for this work.  \nKeywords: artificial intelligence | breast cancer | conventional models | machine learning | risk assessment  \nABSTRACT  \nBackground: Breast cancer (BC) is a major global health concern with rising incidence and mortality rates in many developing countries. Effective BC risk assessment models are crucial for prevention and early detection. While the Gail model, a traditional logistic regression-based model, has been broadly used, its predictive performance may be limited by its linear assumptions. With the rapid advancement of artificial intelligence (AI) in medical sciences, various complex machine learning algorithms have been developed for risk prediction, including for BC.  \nAims: This study aims to compare the quality of AI-based models with the traditional Gail model in assessing BC risk using a population dataset. It also evaluates the performance of these models in predicting BC risk.  \nMethods and Results: This study involved 942 newly diagnosed BC patients and 975 healthy controls at the Cancer Institute in IKH hospital Complex, Tehran. Ten classification algorithms were applied to the dataset. The accuracy, sensitivity, precision, and feature importance in the machine learning algorithms were assessed and compared to previous studies for evaluation. The study found that AI algorithms alone did not significantly improve predictability compared to the Gail model. However, the importance of variables varied significantly among the AI algorithms. Understanding feature importance and interactions is crucial in AI modeling in order to enhance accuracy and identify critical risk factors.  \nConclusion: This study concluded that, in BC risk prediction, incorporating specific risk factors, such as genetic and imagerelated variables, may be necessary to further enhance accuracy in BC risk prediction models. Furthermore, it is crucial to address modeling issues in models with a restricted number of features for future research.  \n\n| Abbreviations: BC, breast cancer; BCRAT, breast cancer risk assessment tool; DT, decision tree; IARC, International Agency for Research on Cancer; KNN, k-nearest neighbor; LR, logistic regression; ML, machine learning; RF, random forest; SVM, support vector machine. |\n| --- |\n| This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and re","cbCaiqqDlvRb0ALH","https://ap.wps.com/l/cbCaiqqDlvRb0ALH","pdf",419327,1,9,"English","en",105,"# Introduction\n## Background and motivation\n## Gail model and logistic regression\n## Machine learning approaches","[{\"question\":\"What is the goal of this study on breast cancer risk prediction?\",\"answer\":\"To compare the quality of AI-based models with the traditional Gail model for assessing breast cancer risk and to evaluate their predictive performance.\"},{\"question\":\"How was the dataset used in the model comparison?\",\"answer\":\"The study used a dataset containing 942 newly diagnosed BC patients and 975 healthy controls, applying ten classification algorithms and comparing accuracy, sensitivity, and precision.\"},{\"question\":\"Did AI models outperform the Gail model in predicting breast cancer risk?\",\"answer\":\"AI algorithms alone did not significantly improve predictability compared with the Gail model, although variable importance differed across AI algorithms.\"}]","Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data - Cancer Reports | PDF",1785899324,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"comparison-of-machine-learning-models-for-classification-of-breast-cancer-risk-based-on-clinical-data-cancer-reports","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/comparison-of-machine-learning-models-for-classification-of-breast-cancer-risk-based-on-clinical-data-cancer-reports/125495/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the goal of this study on breast cancer risk prediction?","Question",{"text":75,"@type":76},"To compare the quality of AI-based models with the traditional Gail model for assessing breast cancer risk and to evaluate their predictive performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset used in the model comparison?",{"text":80,"@type":76},"The study used a dataset containing 942 newly diagnosed BC patients and 975 healthy controls, applying ten classification algorithms and comparing accuracy, sensitivity, and precision.",{"name":82,"@type":73,"acceptedAnswer":83},"Did AI models outperform the Gail model in predicting breast cancer risk?",{"text":84,"@type":76},"AI algorithms alone did not significantly improve predictability compared with the Gail model, although variable importance differed across AI algorithms.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]