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The workflow follows six stages: collecting raw Electronic Medical Records data, revising feature attributes with expert input, preprocessing, adapting the learning pipeline, training CART, Random Forest, and XGBOOST, and evaluating outcomes. Analysis identifies 21 feature attributes linked to early diagnosis. Among tested models, XGBOOST performs best, helping clinicians choose clinical tests and procedures, which can reduce patient waiting time and examination costs, while supporting further applications of machine learning in medicine.",{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-practical-approach-for-colorectal-cancer-diagnosis-based-on-machine-learning/368438/",{"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/a-practical-approach-for-colorectal-cancer-diagnosis-based-on-machine-learning/368438.png","ImageObject",300,407,{"name":92,"@type":93},"Jacob","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-25","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What machine learning models are used for the colorectal cancer diagnosis system?","Question",{"text":112,"@type":113},"The study trains CART, Random Forest, and XGBOOST models and compares their performance for the diagnosis task.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are the 21 feature attributes obtained and what is their purpose?",{"text":117,"@type":113},"Feature attributes are extracted after analyzing experimental measurement parameter values, and they are described as relating to support early diagnosis of colorectal cancer.",{"name":119,"@type":110,"acceptedAnswer":120},"Why is early detection emphasized in the introduction?",{"text":121,"@type":113},"Colorectal cancer can progress through distinct stages often asymptomatically early on, so systematic screening and early detection are pivotal for improving clinical outcomes and reducing mortality.","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},368438,1790714191,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"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":41},962084931830,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","OPEN ACCESS  \nCitation: Hai Minh N, Quy TQ, Tam ND, Tuan TM, Son LH (2025) A practical approach for colorectal cancer diagnosis based on machine learning. PLoS One 20(4): e0321009. [https://](https://)[ ](https://)[doi.org/10.1371/journal.pone.0321009](doi.org/10.1371/journal.pone.0321009)  \nEditor: Jie Zhang, Newcastle University, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND  \nReceived: October 17, 2024  \nAccepted: February 27, 2025  \nPublished: April 29, 2025  \nCopyright: © 2025 Hai Minh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData availability statement: All relevant data are within the paper.  \nFunding: This research was sponsored by the Ministry of Education and Training project,“Research on Application of Machine Learning Model in Analysis Electronic Medical Records Gastrointestinal Disease”, B2022-TNA-24 .  \nRESEARCH ARTICLE  \nA practical approach for colorectal cancer diagnosis based on machine learning  \nNguyen Hai Minh1*, Tran Quang Quy1, Ngo Duc Tam2, Tran Manh Tuan3, Le Hoang Son2  \n1 Thai Nguyen University, Information and Communication Technology, Thai Nguyen, Vietnam, 2 Artificial Intelligence Research Center, VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam, 3 Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam  \n* [nhminh@ictu.edu.vn](nhminh@ictu.edu.vn)  \nAbstract  \nIn this paper, we present the results of applying machine learning models to build a Colorectal Cancer Diagnosis system. The methodology encompasses six key steps: collecting raw data from Electronic Medical Records (EMRs), revising feature attributes with expert input, data preprocessing, model adaptation, training machine learning models (CART, Random Forest, and XGBOOST), and evaluating the results. Furthermore, based on analysis of experimental measurement parameter values, 21 feature attributes which relate to support early diagnose the Colorectal cancer disease are extracted. Among different models implemented in our case, XGBOOST is the most suitable model to solve this problem. The system assists clinicians to select clinical tests and medical procedures for a colorectal cancer patient. Therefore, patients can save the waiting time and medical examination costs. On the other hand, based on the achievements from this research, our approach can guide further applying machine learning in medicine.  \n1. Introduction  \nColorectal colon, with the third highest diagnosis rate, is the second dangerous cancer in Viet Nam. Colorectal cancer constitutes a substantial public health challenge, particularly among men. Originating from malignant cells within the rectum, a segment of the large intestine, colorectal cancer progresses through distinct stages, often asymptomatically during its early phases. Early detection via systematic screening is pivotal for improving clinical outcomes and reducing mortality. However, traditional diagnostic modalities, including endoscopy and pathological biopsy, are hindered by their significant time, cost, and accessibility constraints [ 1 ,2] . Diagnosing is one of the core principles of medicine based on the integration of multi-source data analysis and clinician experience. Because of the variety of tumor symptoms, rapid tumor growth, individual differences, and drug sensitivity, it is difficult for doctors to diagnose tumors accurately. Artificial Intelligence (AI) supports clinicians in qualitative  \nPLOS One | [https://doi.org/10.1371/journal.pone.0321009](https://doi.org/10.1371/journal.pone.0321009) April 29, 2025 1 / 12  \nCompeting interests: The authors have declared that no competing interests exist.  \ndiagnosis and detecting the stage of colon cancer, which currently relies on endoscopy and pathological biopsy [3] . Colonoscopy is conside","cbCaiquO3GOyzrGN","https://ap.wps.com/l/cbCaiquO3GOyzrGN","pdf",789578,12,"English","# Abstract\n# 1. Introduction","[{\"question\":\"What machine learning models are used for the colorectal cancer diagnosis system?\",\"answer\":\"The study trains CART, Random Forest, and XGBOOST models and compares their performance for the diagnosis task.\"},{\"question\":\"How are the 21 feature attributes obtained and what is their purpose?\",\"answer\":\"Feature attributes are extracted after analyzing experimental measurement parameter values, and they are described as relating to support early diagnosis of colorectal cancer.\"},{\"question\":\"Why is early detection emphasized in the introduction?\",\"answer\":\"Colorectal cancer can progress through distinct stages often asymptomatically early on, so systematic screening and early detection are pivotal for improving clinical outcomes and reducing mortality.\"}]","A practical approach for colorectal cancer diagnosis based on machine learning | PDF",1790176938]