[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123526-en":3,"doc-seo-123526-105":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},123526,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Machine Learning Approaches for Predicting Breast Cancer Recurrence - A Comparative Analysis","This paper reports a comparative analysis of four supervised machine learning algorithms—Random Forest, Support Vector Machines (radial and linear kernels), Logistic Regression, and Multi-Layer Perceptron—for breast cancer recurrence prediction on a curated clinical dataset. After identifier elimination and z-score normalization, the data were stratified and split into 80:20 training and testing sets. Performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC. Feature-importance methods indicated lymph node count, survival time, and hormone receptor levels as significant predictors.","Mesopotamian Journal of Artificial Intelligence in Healthcare Vol.2025, pp. 208–218  \nDOI: [https://doi.org/10.58496/MJAIH/2025/020](https://doi.org/10.58496/MJAIH/2025/020) ; ISSN: 3005-365X [https://mesopotamian.press/journals/index.php/MJAIH](https://mesopotamian.press/journals/index.php/MJAIH)  \nResearch Article  \nMachine Learning Approaches for Predicting Breast Cancer Recurrence: A Comparative Analysis  \nNoor Razzaq Abbas 1, , Hussein Alkattan2,3,*,, Isam Bahaa Aldallal4, ,  \n1 Al-Furat Al-Awsat Technical University, Technical Institute of Najaf, Najaf, Iraq  \n2 Department of System Programming, South Ural State University, Chelyabinsk, Russia  \n3 Directorate of Environment in Najaf, Ministry of Environment, Najaf, Iraq  \n4 Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey  \nARTICLE INFO  \nArticle History  \nReceived 05 May 2025 Revised 03 Jun 2025 Accepted 08 Jul 2025 Published 30 Jul 2025  \nKeywords  \nBreast cancer recurrence Machine Learning Multi-Layer Perceptron Feature Importance ROC curve  \nABSTRACT  \nThis paper reports a comparative analysis of four supervised machine learning algorithms: RF, SVM (using radial and linear kernels), Logistic Regression, and Multi-Layer Perceptron, for breast cancer recurrence prediction on a carefully curated clinical dataset. The data set, first collected by Royston and Altman and subsequently released on Kaggle, has patient age, menopausal status, tumor size, histological grade, lymph node status, estrogen and progesterone receptor levels, hormone therapy for treatment, recurrence-free survival time, and a binary recurrence outcome. The data set was then divided after the elimination of identifiers and z-score normalization in an 80:20 ratio using stratified sampling. Models were compared based on accuracy, precision, recall, F1-score, and area under the ROC curve, with RFand Logistic Regression having the highest test-set accuracy of 0.703. Feature significance analysis Gini impurity in R F, linear model absolute coefficients, and permutation importance in neural networks all showed lymph node count, survival time, and hormone receptor levels to be significant predictors. Visualized confusion matrices, ROC curves, and correlation heatmaps enhanced interpretability. The results illustrate the potential of explainable machine learning to enhance individualized surveillance and treatment planning in breast cancer care.  \n1. INTRODUCTION  \nBreast cancer remains a formidable global health issue, the most frequently diagnosed malignancy and second most common cause of cancer-related death in women across the globe. In 2018, the GLOBOCAN project had projected 2.1 million new instances of breast cancer and 627 000 mortalities in 185 nations, demonstrating the urgent need for improved prediction and care [1]. Population-based screening programmers have undoubtedly reduced mortality but create ethical, psychological, and economic problems around overdiagnosis and individualized vs. standard screening intervals. The \"My Personal Breast Screening\" (MyPeBS) randomized trial, for example, compares the impact of risk-adapted screening on health outcomes with standard annual mammography, highlighting patient choice and resource use considerations [2]. Concurrently, the WISDOM Study will resolve disputes around imaging test frequency by comparing fixed yearly regimens to dynamic, riskadapted protocols [3]. Outside screening, advances in prognostic staging have been proposed to more accurately capture heterogeneity of breast cancer. Recent revisions to the tumor–node–metastasis (TNM) classification add additional biomarkers and molecular subtypes to assist individualized therapeutic choice-making [4]. Recurrence of disease is a continued driver of morbidity and mortality despite these advances. A population-based study with numerous cases from the Netherlands offered ten-year recurrence rates by subtype with the best prognosis being seen in luminal A tumors w","cbCaikIymd4mlWB3","https://ap.wps.com/l/cbCaikIymd4mlWB3","pdf",1109048,1,11,"English","en",105,"# Introduction\n## Breast cancer burden and screening challenges\n## Prognostic staging and recurrence drivers\n## Hormone receptors and TNBC as risk determinants\n## Motivation for machine learning and explainable AI\n# Methods (implied)\n## Algorithms compared\n## Dataset preparation and split strategy\n## Evaluation metrics and feature-importance analysis\n# Results (implied)\n## Accuracy and ROC-AUC comparison\n## Key predictors identified\n## Interpretability via visualizations\n# Conclusion (implied)","[{\"question\":\"Which machine learning models are compared for breast cancer recurrence prediction?\",\"answer\":\"The study compares Random Forest, SVM with radial and linear kernels, Logistic Regression, and a Multi-Layer Perceptron.\"},{\"question\":\"How was the dataset prepared and split for training and testing?\",\"answer\":\"Identifiers were eliminated and z-score normalization was applied, then the dataset was stratified and split into an 80:20 ratio.\"},{\"question\":\"What features were identified as significant predictors of recurrence?\",\"answer\":\"Feature-significance analyses highlighted lymph node count, survival time, and hormone receptor levels as significant predictors.\"}]","Machine Learning Approaches for Predicting Breast Cancer Recurrence - A Comparative Analysis | PDF",1785817122,28,{"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},"machine-learning-approaches-for-predicting-breast-cancer-recurrence-a-comparative-analysis","",{"@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/machine-learning-approaches-for-predicting-breast-cancer-recurrence-a-comparative-analysis/123526/",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","2026-08-04",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},"Which machine learning models are compared for breast cancer recurrence prediction?","Question",{"text":75,"@type":76},"The study compares Random Forest, SVM with radial and linear kernels, Logistic Regression, and a Multi-Layer Perceptron.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset prepared and split for training and testing?",{"text":80,"@type":76},"Identifiers were eliminated and z-score normalization was applied, then the dataset was stratified and split into an 80:20 ratio.",{"name":82,"@type":73,"acceptedAnswer":83},"What features were identified as significant predictors of recurrence?",{"text":84,"@type":76},"Feature-significance analyses highlighted lymph node count, survival time, and hormone receptor levels as significant predictors.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]