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The study extracts clinical and histopathological information from unstructured medical records. K-Means clustering identifies patient subgroups with different tumor aggressiveness, followed by Cox proportional hazards survival analysis. Two subgroups are reported with high-performing classifiers and contrasting recall/precision trade-offs. Future work should validate across institutions, add molecular biomarkers, and explore deep learning.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-approaches-for-predicting-breast-cancer-recurrence-using-clinical-and-histopathological-data-research/440899/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-approaches-for-predicting-breast-cancer-recurrence-using-clinical-and-histopathological-data-research/440899.png","ImageObject",300,407,{"name":92,"@type":93},"Emma Wilson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-01","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What data is used to predict breast cancer recurrence?","Question",{"text":112,"@type":113},"The study extracts clinical and histopathological information from unstructured medical records of breast cancer patients.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are patient subgroups identified in the proposed approach?",{"text":117,"@type":113},"A clustering technique, K-Means, is used to separate patients into groups with differing tumor aggressiveness profiles.",{"name":119,"@type":110,"acceptedAnswer":120},"Which models are used for survival analysis and subgroup performance?",{"text":121,"@type":113},"Survival outcomes are analyzed with the Cox proportional hazards model; for less aggressive tumors, Quadratic Discriminant Analysis yields the highest recall, while Random Forest shows the best recall–precision trade-off for more aggressive tumors.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},440899,1790840257,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},3848291630094,"https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Clinical and Experimental Medicine (2026) 26:73  \n[https://doi.org/10.1007/s10238-025-02018-x](https://doi.org/10.1007/s10238-025-02018-x)  \nRESEARCH  \nMachine learning approaches for predicting breast cancer recurrence using clinical and histopathological data  \nMohd Abas Bhat1 · Mushtaq Ahmad Mir2 · R. Vijaya Lakshmi3 · Tejaswini Pradhan4 · G. V. V. Jagannadha Rao5 · Ghanshyam G. Tejani6,7 · Syed Abid Hussain8,9  \nReceived: 28 August 2025 / Accepted: 17 December 2025 © The Author(s) 2025  \nAbstract  \nBreast cancer remains the most common malignancy among women worldwide, with recurrence representing a major clinical challenge. Although significant progress has been made in early detection and treatment, recurrence affects up to 40% of patients in Brazil, influencing survival outcomes and therapeutic decisions. In this context, Machine Learning offers valuable potential for enhancing recurrence prediction by enabling data-driven risk assessment and personalized patient care. In the present study, clinical and histopathological information was extracted from unstructured medical records of breast cancer patients. A clustering technique (K-Means) was applied to identify patient subgroups with varying tumor aggressiveness profiles. Survival outcomes were further analyzed using the Cox proportional hazards model. Two distinct subgroups were identified: for less aggressive tumors, Quadratic Discriminant Analysis achieved a remarkably high recall of 0.9872, while for more aggressive tumors, Random Forest provided the most favorable trade-off between recall (0.7296) and precision (0.6811) . Future research should explore validation across multiple institutions, incorporate molecular biomarkers, and leverage deep learning approaches to enhance predictive performance.  \nKeywords Breast cancer recurrence prediction · Breast cancer survival analysis model · Machine learning in breast cancer  \nIntroduction  \nThe global burden of breast cancer continues to grow, with more than 2 million new cases expected each year, making it one of the leading causes of cancer-related deaths among women worldwide [1] .“In the United States, nearly 13% of women—about 1 in 8—are projected to develop invasive  \n􀀍 Syed Abid Hussain [dr.abid@bakhtar.edu.af](dr.abid@bakhtar.edu.af)  \n1 Department of Economics, Kashmir University, Srinagar 190006, India  \n2 Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia  \n3 Faculty of Management Studies, ICFAI University, Raipur 490042, Chhattisgarh, India  \n4 Faculty of Mathematics, Kalinga University, Raipur 492001, Chhattisgarh, India  \nbreast cancer during their lifetime” [2] . Similarly, in Brazil, breast cancer is the most commonly diagnosed cancer among women (excluding non-melanoma skin tumors), with approximately 74,000 new cases anticipated between 2023 and 2025 [3] .  \nAlthough improvements in screening, early detection, and individualized treatment have enhanced survival  \n5 Faculty of Mathematics, ICFAI University, Raipur 490042, Chhattisgarh, India  \n6 department of industrial engineering and management, Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan, 320315, Taiwan, Taoyuan 320315, Taiwan  \n7 Applied Science Research Centre, Applied Science Research Center, Applied Science Private University, Amman, 11937, Jordan, Amman 11937, Jordan  \n8 Department of Computer Science and Engineering, Bakhtar University, Kart e Char, Kabul 1001, Afghanistan  \n9 University Centre for Research & Development, Chandigarh University, Gharuan, Mohali, Punjab140413, India  \n1 3  \noutcomes [4] . recurrence remains a significant clinical concern. It affects nearly 30% of patients globally and up to 40% in Brazil, undermining both prognosis and quality of life [3, 5] . Notably, late recurrence can occur even decades after the initial diagnosis up to 32 years often influenced by the tumor’s baseline characteristics [6] .  \nEm","cbCaidrCFzfcSXof","https://ap.wps.com/l/cbCaidrCFzfcSXof","pdf",1434790,15,"English","# Abstract\n# Introduction\n# Methodology\n## Ethical compliance and participant consent\n## Data Availability","[{\"question\":\"What data is used to predict breast cancer recurrence?\",\"answer\":\"The study extracts clinical and histopathological information from unstructured medical records of breast cancer patients.\"},{\"question\":\"How are patient subgroups identified in the proposed approach?\",\"answer\":\"A clustering technique, K-Means, is used to separate patients into groups with differing tumor aggressiveness profiles.\"},{\"question\":\"Which models are used for survival analysis and subgroup performance?\",\"answer\":\"Survival outcomes are analyzed with the Cox proportional hazards model; for less aggressive tumors, Quadratic Discriminant Analysis yields the highest recall, while Random Forest shows the best recall–precision trade-off for more aggressive tumors.\"}]","Machine learning approaches for predicting breast cancer recurrence using clinical and histopathological data - Research | PDF",1790693845,38]