[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119894-en":3,"doc-seo-119894-105":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},119894,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","On the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility","Classical survival analysis assumes every subject will eventually experience the event of interest, which becomes inadequate when a subset will never experience it. This work addresses adaptation of machine learning methods for survival estimation under a cure scenario. It reviews cure models and recent machine learning methodologies, then introduces an approach that accounts for cured individuals. An extensive simulation study compares adapted algorithms with existing cure models, and results indicate good behavior of semiparametric and nonparametric strategies depending on the scenario. Real dataset illustrations further demonstrate practical value and highlight performance issues for small samples.","mathematics  \nArticle  \nOn the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility  \nAna Ezquerro 1, Brais Cancela 2 and Ana López-Cheda 3, *  \nCitation: Ezquerro, A.; Cancela, B.; López-Cheda, A. On the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility. Mathematics 2023, 11, 4150 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)math11194150  \nAcademic Editor: Stefano Bonnini  \nReceived: 4 September 2023  \nRevised: 28 September 2023  \nAccepted: 1 October 2023  \nPublished: 2 October 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Faculty of Informatics, University of A Coruña, 15071 A Coruña, Spain; [ana.ezquerro@udc.es](ana.ezquerro@udc.es)  \n2 CITIC, LIDIA Group, Department of Computer Science, University of A Coruña, 15071 A Coruña, Spain; [brais.cancela@udc.es](brais.cancela@udc.es)  \n3 CITIC, MODES Group, Department of Mathematics, University of A Coruña, 15071 A Coruña, Spain  \n* Correspondence: [ana.lopez.cheda@udc.es](ana.lopez.cheda@udc.es)  \nAbstract: In classical survival analysis, it is assumed that all the individuals will experience the event of interest. However, if there is a proportion of subjects who will never experience the event, then a standard survival approach is not appropriate, and cure models should be considered instead. This paper deals with the problem of adapting a machine learning approach for classical survival analysis to a situation when cure (i.e., not suffering the event) is a possibility. Speci􀀂cally, a brief review of cure models and recent machine learning methodologies is presented, and an adaptation of machine learning approaches to account for cured individuals is introduced. In order to validate the proposed methods, we present an extensive simulation study in which we compare the performance of the adapted machine learning algorithms with existing cure models. The results show the good behavior of the semiparametric or the nonparametric approaches, depending on the simulated scenario. The practical utility of the methodology is showcased through two real-world dataset illustrations. In the 􀀂rst one, the results show the gain of using the nonparametric mixture cure model approach. In the second example, the results show the poor performance of some machine learning methods for small sample sizes.  \nKeywords: censored data; cure rate; deep learning; mixture cure models; simulation; system reliability  \nMSC: 62N02  \n1. Introduction to Survival Analysis  \nSurvival analysis is a branch of statistics whose object of study is the elapsed time until an event of interest. Depending on its application, survival analysis has been widely used in many areas: engineering (time to failure of a machine), economics (duration of unemployment), medicine (time to death due to a speci􀀂c medical condition), etc. A relevant characteristic in survival analysis is that the event of interest does not always occur during the follow-up period or that the failure time cannot be exactly de􀀂ned. This absence of information leads to the concept of censoring. The main challenge in survival analysis is how to handle censored data. Speci􀀂cally, there are two common cases of censoring. On the one hand, point censoring occurs when the individual does not experience the event within the study period. Two types of point censoring can be de􀀂ned: right censoring (the event of interest is experienced after the end of study, since it is not possible to follow the individuals for an in􀀂nite period of time) and left censoring (it is known that the failure occurred prior to the start of follow-up) . On the o","cbCaiizhc7mPSdCC","https://ap.wps.com/l/cbCaiizhc7mPSdCC","pdf",710596,1,21,"English","en",105,"# Introduction to Survival Analysis\n## Censoring in survival data\n# Cure Models and Survival Analysis\n## Concept of cured individuals\n# Machine Learning Adaptations for Cure Scenarios\n## Accounting for cured subjects\n# Validation and Results\n## Simulation study comparison\n## Real-world dataset illustrations","[{\"question\":\"Why is classical survival analysis sometimes inappropriate?\",\"answer\":\"Classical survival analysis assumes all individuals will eventually experience the event of interest. When a proportion of subjects will never experience the event, standard methods no longer match the data-generating process.\"},{\"question\":\"What does “cure” mean in cure models?\",\"answer\":\"In this context, cure does not imply absence of illness. It means individuals will not suffer the event of interest, even if followed for a long time.\"},{\"question\":\"How are the proposed machine learning methods validated?\",\"answer\":\"The paper validates the methods using an extensive simulation study that compares adapted machine learning algorithms with existing cure models, and it also demonstrates utility through two real-world dataset illustrations.\"}]","On the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility | PDF",1785726876,53,{"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},"on-the-reliability-of-machine-learning-models-for-survival-analysis-when-cure-is-a-possibility","",{"@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/on-the-reliability-of-machine-learning-models-for-survival-analysis-when-cure-is-a-possibility/119894/",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","2026-08-03",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},"Why is classical survival analysis sometimes inappropriate?","Question",{"text":75,"@type":76},"Classical survival analysis assumes all individuals will eventually experience the event of interest. When a proportion of subjects will never experience the event, standard methods no longer match the data-generating process.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does “cure” mean in cure models?",{"text":80,"@type":76},"In this context, cure does not imply absence of illness. It means individuals will not suffer the event of interest, even if followed for a long time.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the proposed machine learning methods validated?",{"text":84,"@type":76},"The paper validates the methods using an extensive simulation study that compares adapted machine learning algorithms with existing cure models, and it also demonstrates utility through two real-world dataset illustrations.","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"]