[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122187-en":3,"doc-seo-122187-105":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":20,"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},122187,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","The impact of dietary fiber on colorectal cancer patients based on machine learning - Original Research","This original research evaluates how enteral nutrition supplemented with dietary fiber affects patients undergoing laparoscopic colorectal cancer surgery. From January 2023 to August 2024, 164 CRC patients were randomly assigned to standard nutritional care or a fiber-containing observation intervention and followed through day 14 postoperatively. The study compares inflammatory markers, infection stress indicators, nutritional parameters, intestinal recovery, and complications, then builds and tests four machine learning models to predict immune and inflammation outcomes, highlighting key predictors and model performance.","OPEN ACCESS  \nEDITED BY  \nEvelyn Nunes Goulart Da Silva Pereira, Oswaldo Cruz Foundation (Fiocruz), Brazil  \nREVIEWED BY  \nEmmanouella Magriplis,  \nAgricultural University of Athens, Greece Salvatore Vaccaro,  \nIRCCS Local Health Authority of Reggio Emilia, Italy  \n*CORRESPONDENCE  \nWeicai Cheng  \n [weicaicheng0@tutamail.com](weicaicheng0@tutamail.com)  \nRECEIVED 09 October 2024  \nACCEPTED 06 January 2025  \nPUBLISHED 24 January 2025  \nCITATION  \nJi X, Wang L, Luan P, Liang J and  \nCheng W (2025) The impact of dietary fiber on colorectal cancer patients based on machine learning.  \nFront. Nutr. 12:1508562 .  \ndoi: 10.3389/fnut.2025.1508562  \nCOPYRIGHT  \n© 2025 Ji, Wang, Luan, Liang and Cheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nTYPE Original Research PUBLISHED 24 January 2025 DOI 10.3389/fnut.2025.1508562  \nThe impact of dietary fiber on colorectal cancer patients based on machine learning  \nXinwei Ji, Lixin Wang, Pengbo Luan, Jingru Liang and Weicai Cheng *  \nDepartment of Gastrointestinal Surgery, Yantaishan Hospital, Yantai, China  \nObjective: This study aimed to evaluate the impact of enteral nutrition with dietary fiber on patients undergoing laparoscopic colorectal cancer (CRC) surgery.  \nMethods: Between January 2023 and August 2024, 164 CRC patients were randomly assigned to two groups at our hospital. The control group received standard nutritional intervention, while the observation group received enteral nutritional support containing dietary fiber. Both groups were subjected to intervention and continuously observed until the 14th postoperative day. An observational analysis assessed the impact of dietary fiber intake on postoperative nutritional status in CRC patients. The study compared infection stress index, inflammatory factors, nutritional status, intestinal function recovery, and complication incidence between groups. Additionally, four machine learning models—Logistic Regression (LR), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM)—were developed based on nutritional and clinical indicators.  \nResults: In the observation group, levels of procalcitonin (PCT), beta-endorphin (β-EP), C-reactive protein (CRP), interleukin-1 (IL-1), interleukin-8 (IL-8), and tumor necrosis factor-alpha (TNF-α) were significantly lower compared to the control group (p \u003C 0.01) . Conversely, levels of albumin (ALB), hemoglobin (HB), transferrin (TRF), and prealbumin (PAB) in the observation group were significantly higher than those in the control group (p \u003C 0.01) . Furthermore, LR, RF, NN, and SVM models can effectively predict the effects of dietary fiber on the immune function and inflammatory response of postoperative CRC patients, with the NN model performing the best. Through the screening of machine learning models, four key predictors for CRC patients were identified: PCT, PAB, ALB, and IL-1 .  \nConclusion: Postoperative dietary fiber administration in colorectal cancer enhances immune function, reduces disease-related inflammation, and inhibits tumor proliferation. Machine learning-based CRC prediction models hold clinical value.  \nKEYWORDS  \ncolorectal cancer, dietary fiber, enteral nutrition support, nutritional status, machine learning  \nFrontiers in Nutrition 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nColorectal cancer (CRC) ranks as the third most prevalent malignancy worldwide, and its incidence rate is on the rise ( 1) . In 2018, there were 18.1 million new cancer cases globally, with CRC ranking fourth among them. Given this prevalence, understanding the pathogenesis o","cbCaifwJRPmH4UgE","https://ap.wps.com/l/cbCaifwJRPmH4UgE","pdf",505393,1,11,"English","en",105,"# Introduction\n## Rationale and significance of colorectal cancer\n## Dietary factors and inflammation in CRC\n## Postoperative role of dietary fiber\n# Methods\n## Study design and patient allocation\n## Intervention and follow-up\n## Outcome measures\n## Machine learning model development\n# Results\n## Inflammatory and immune marker changes\n## Nutritional parameter changes\n## Predictive performance and key predictors\n# Conclusion","[{\"question\":\"What intervention does the study assess for colorectal cancer patients?\",\"answer\":\"The study evaluates enteral nutrition support supplemented with dietary fiber, compared with standard nutritional intervention after laparoscopic CRC surgery.\"},{\"question\":\"Which outcomes are used to judge the impact of dietary fiber?\",\"answer\":\"Outcomes include infection stress index, inflammatory factors, nutritional status, intestinal function recovery, and postoperative complication incidence, assessed through the 14th postoperative day.\"},{\"question\":\"How do the machine learning models contribute to the study?\",\"answer\":\"Four models (LR, RF, NN, SVM) are developed using nutritional and clinical indicators to predict dietary-fiber-related effects on immune function and inflammatory response, with the neural network performing best and key predictors identified.\"}]","The impact of dietary fiber on colorectal cancer patients based on machine learning - 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