[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128097-en":3,"doc-seo-128097-105":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},128097,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Association Between Risk Factors and Major Cancers - Explainable Machine Learning Approach","Major cancers are increasing in incidence, prompting a need for earlier screening and more precise monitoring of risk factors to manage cancer risk. This study applies explainable machine learning models to identify and analyze key risk factors linked to breast, colorectal, lung, and prostate cancers. Using deidentified MIMIC-III longitudinal records and propensity score matching, penalized logistic regression, random forest, and MLP models rank risk factors with feature-importance and similarity analyses.","San Jose State University  \nSJSU ScholarWorks  \nFaculty Research, Scholarly, and Creative Activity  \n1-1-2025  \nAssociation Between Risk Factors and Major Cancers: Explainable Machine Learning Approach  \nXiayuan Huang Yale University  \nShushun Ren  \nUniversity of Michigan School of Nursing  \nXinyue Mao  \nUniversity of Michigan, Ann Arbor  \nSirui Chen  \nUniversity of Michigan, Ann Arbor  \nElle Chen  \nUniversity of Michigan School of Nursing  \nSee next page for additional authors  \nFollow this and additional works at: [https://scholarworks.sjsu.edu/faculty_rsca](https://scholarworks.sjsu.edu/faculty_rsca)  \nRecommended Citation  \nXiayuan Huang, Shushun Ren, Xinyue Mao, Sirui Chen, Elle Chen, Yuqi He, and Yun Jiang. \"Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach\" Jmir Cancer (2025) .  \n[https://doi.org/10.2196/62833](https://doi.org/10.2196/62833)  \nThis Article is brought to you for free and open access by SJSU ScholarWorks. It has been accepted for inclusion in Faculty Research, Scholarly, and Creative Activity by an authorized administrator of SJSU ScholarWorks. For more information, please contact [scholarworks@sjsu.edu](scholarworks@sjsu.edu).  \nAuthors  \nXiayuan Huang, Shushun Ren, Xinyue Mao, Sirui Chen, Elle Chen, Yuqi He, and Yun Jiang  \nThis article is available at SJSU ScholarWorks: [https://scholarworks.sjsu.edu/faculty_rsca/6291](https://scholarworks.sjsu.edu/faculty_rsca/6291)  \nJMIR CANCER Huang et al  \nOriginal Paper  \nAssociation Between Risk Factors and Major Cancers: Explainable Machine Learning Approach  \n\n| Xiayuan Huang 1 , PhD; Shushun Ren2 , MS; Xinyue Mao3 , BS; Sirui Chen3 ; Elle Chen2 ; Yuqi He4 , PhD; Yun Jiang2 , MS, PhD |\n| --- |\n| 1Department of Biostatistics, Yale University, New Haven, CT, United States\u003Cbr>2School of Nursing, University of Michigan–Ann Arbor, Ann Arbor, MI, United States\u003Cbr>3College of Literature Science and the Arts, University of Michigan–Ann Arbor, Ann Arbor, MI, United States 4University Library, San Jose State University, San Jose, CA, United States\u003Cbr>Corresponding Author:\u003Cbr>Yun Jiang, MS, PhD School of Nursing\u003Cbr>University of Michigan–Ann Arbor\u003Cbr>400 North Ingalls Street Ann Arbor , MI, 48109 United States\u003Cbr>Phone: 1 7347633705\u003Cbr>Fax: 1 7346472416\u003Cbr>Email: [jiangyu@umich.edu](jiangyu@umich.edu)\u003Cbr>Abstract |\n\nBackground: Cancer is a life-threatening disease and a leading cause of death worldwide, with an estimated 611,000 deaths and over 2 million new cases in the United States in 2024. The rising incidence of major cancers, including among younger individuals, highlights the need for early screening and monitoring of risk factors to manage and decrease cancer risk.  \nObjective: This study aimed to leverage explainable machine learning models to identify and analyze the key risk factors associated with breast, colorectal, lung, and prostate cancers . By uncovering significant associations between risk factors and these major cancer types, we sought to enhance the understanding of cancer diagnosis risk profiles. Our goal was to facilitate more precise screening, early detection, and personalized prevention strategies, ultimately contributing to better patient outcomes and promoting health equity.  \nMethods: Deidentified electronic health record data from Medical Information Mart for Intensive Care (MIMIC)–III was used to identify patients with 4 types of cancer who had longitudinal hospital visits prior to their diagnosis presence. Their records were matched and combined with those of patients without cancer diagnoses using propensity scores based on demographic factors. Three advanced models, penalized logistic regression, random forest, and multilayer perceptron (MLP), were conducted to identify the rank of risk factors for each cancer type, with feature importance analysis for random forest and MLP models. The rank biased overlap was adopted to compare the similarity of ranked risk factors across cancer types.  \nResults: O","cbCaiab1j97obSqE","https://ap.wps.com/l/cbCaiab1j97obSqE","pdf",865520,5,1,17,"English","en",105,"# Abstract\n## Background\n## Objective\n## Methods\n## Results\n## Conclusions","[{\"question\":\"Which cancer types were analyzed in this study?\",\"answer\":\"The study focused on breast, colorectal, lung, and prostate cancers. It aimed to identify risk factors specific to each major cancer type.\"},{\"question\":\"What data source and matching approach were used?\",\"answer\":\"The study used deidentified electronic health records from MIMIC-III. Patients with cancer were matched to those without cancer using propensity scores based on demographic factors.\"},{\"question\":\"How did the models perform and which model was best?\",\"answer\":\"The MLP model achieved the best overall performance, with AUC values of 0.78 for breast, 0.76 for colorectal, 0.84 for lung, and 0.78 for prostate cancer. It outperformed baseline models with P\\u003c.001.\"}]","Association Between Risk Factors and Major Cancers - Explainable Machine Learning Approach | PDF",1785944782,43,{"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},"association-between-risk-factors-and-major-cancers-explainable-machine-learning-approach","",{"@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/association-between-risk-factors-and-major-cancers-explainable-machine-learning-approach/128097/",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","2026-08-27","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},"Which cancer types were analyzed in this study?","Question",{"text":77,"@type":78},"The study focused on breast, colorectal, lung, and prostate cancers. It aimed to identify risk factors specific to each major cancer type.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What data source and matching approach were used?",{"text":82,"@type":78},"The study used deidentified electronic health records from MIMIC-III. Patients with cancer were matched to those without cancer using propensity scores based on demographic factors.",{"name":84,"@type":75,"acceptedAnswer":85},"How did the models perform and which model was best?",{"text":86,"@type":78},"The MLP model achieved the best overall performance, with AUC values of 0.78 for breast, 0.76 for colorectal, 0.84 for lung, and 0.78 for prostate cancer. 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