[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126068-en":3,"doc-seo-126068-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126068,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine learning methods based on mammogram images to estimate survival times for breast cancer patients - Research paper","Predictive survival modeling using radiological images supports medical diagnosis by identifying influencing factors and estimating risk for individual patients. This study uses mammogram images from breast cancer patients in Iraq and applies machine learning to estimate patient survival. Features are extracted with Fast independent component analysis (Fast ICA) and Nonnegative Matrix Factorization (NMF). Survival prediction models combine Random Survival Forests and Support Vector Machines, then compare performance via MSE and C-Index to select the best approach.","Machine learning methods based on mammogram images to estimate survival times for breast cancer patients  \nNoor Ayad Mohammed, Entsar Arebe Fadam  \nDepartment of statistics, College of Administration and Economics, University of Baghdad, Baghdad, Iraq  \n\n| ABSTRACT |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- |\n| Estimating survival times based on medical images resulting from radiological imaging of some parts of the body affected by tumors and making a predictive system for them is considered one of the very important fields at the present time. This is because radiological imaging is one of the first and most important stages of medical diagnosis. Therefore the process of linking medical images including them within the work steps of the statistical analysis for estimating survival times is a modern and important topic. Its importance is helping doctors and medical specialists to determine the influencing factors and risk percentage associated with survival for each patient based on the medical image of the affected part.\u003Cbr>In this paper, the medical images extracted from the mammogram device for breast cancer patients in Iraq. These images were included in the machine learning method for estimating survival of patients. Based on two methods to extract features, The first one is the Fast independent component algorithm (Fast ICA algorithm) and the second one is Nonnegative Matrix Factorization (NMF algorithm) . With two machine learning algorithms, The first method is Random survival forests algorithm and the second method is support vector machine algorithm SVM (SVM) .\u003Cbr>Through the application of the supervised machine learning method on mammogram images of patients with breast cancer, it was found that the best model for estimating survival according to the mean square error (MSE) and concordance index (C-Index) criterion is the model resulting from the use of the Fast ICA algorithm with the random survival forest algorithm compared with the other three models. Accordingly, It is recommended that interested medical agencies and institutions to adopt this model. |  |  |  |  |  |  |\n| Keywords: | Survival function, Feature images, Breast cancer. | extraction, | Machine | learning, | Mammogram | medical |\n| Corresponding Author:\u003Cbr>Noor A. Mohammed\u003Cbr>Department of statistics\u003Cbr>College of Administration and Economics, University of Baghdad.\u003Cbr>Baghdad, Iraq\u003Cbr>[E-mail: noor.mohammed1101@coadec.uobaghdad.edu.iq](E-mail: noor.mohammed1101@coadec.uobaghdad.edu.iq) |  |  |  |  |  |  |\n\n1.Introduction  \nThe process of building a predictive system for patient survival based on modern artificial intelligence methods represented by the machine learning method. This is one of the topics of great and increasing importance at the present time. Due to the advantages that this method possesses that made it superior to the common statistical methods. In estimating survival times, the learning method is accurate, easy to implement, and the ability to deal with big data represented by images. The images can be medically related to patients with tumors or other medical conditions that are diagnosed through radiography, such as scanners, mammograms, etc. Also it is possible that the images used are of space and satellite images. This method has the ability to deal with related variables that are difficult to deal with in traditional statistical models used in estimating survival.  \nThe process of building the predictive system is based on selecting the best predictive model has been used to estimate survival time. Iraqi data set is a group of breast cancer patients who were diagnosed by oncologists at  \nthe Medical City Hospital in Baghdad. Mammogram images for all of these patients. They are numbered 100 patients included within the method of machine learning to estimate their survival.  \nThe application of the machine learning method to estimate survival involves several stages. The first important stage is the availabi","cbCaicXG9NwSWHe5","https://ap.wps.com/l/cbCaicXG9NwSWHe5","pdf",661576,6,1,11,"English","en",105,"# Introduction\n## Predictive survival modeling and motivation\n## Data set description and preprocessing stages\n## Feature extraction methods\n## Machine learning models and evaluation criteria","[{\"question\":\"What data and images are used to estimate breast cancer survival times?\",\"answer\":\"The study uses mammogram images from 100 breast cancer patients diagnosed in Baghdad, Iraq.\"},{\"question\":\"Which feature extraction algorithms are compared in the paper?\",\"answer\":\"Fast ICA (Fast independent component algorithm) and NMF (Nonnegative Matrix Factorization) are used to extract features from mammogram images.\"},{\"question\":\"Which model combination performed best and how was it evaluated?\",\"answer\":\"The Fast ICA features with the Random Survival Forest algorithm achieved the best results, evaluated using MSE and the concordance index (C-Index).\"}]","Machine learning methods based on mammogram images to estimate survival times for breast cancer patients - Research paper | PDF",1785902897,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-methods-based-on-mammogram-images-to-estimate-survival-times-for-breast-cancer-patients-research-paper","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-methods-based-on-mammogram-images-to-estimate-survival-times-for-breast-cancer-patients-research-paper/126068/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-21","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What data and images are used to estimate breast cancer survival times?","Question",{"text":77,"@type":78},"The study uses mammogram images from 100 breast cancer patients diagnosed in Baghdad, Iraq.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which feature extraction algorithms are compared in the paper?",{"text":82,"@type":78},"Fast ICA (Fast independent component algorithm) and NMF (Nonnegative Matrix Factorization) are used to extract features from mammogram images.",{"name":84,"@type":75,"acceptedAnswer":85},"Which model combination performed best and how was it evaluated?",{"text":86,"@type":78},"The Fast ICA features with the Random Survival Forest algorithm achieved the best results, evaluated using MSE and the concordance index (C-Index).","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]