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ORIGINAL RESEARCH article

Front. Nutr.
Sec. Clinical Nutrition
Volume 12 - 2025 | doi: 10.3389/fnut.2025.1508562
This article is part of the Research Topic Dietary Habits in Liver Health and Disease: Preclinical and Clinical Studies View all 10 articles

The impact of dietary fiber on colorectal cancer patients based on machine learning

Provisionally accepted
Xinwei Ji Xinwei Ji Lixin Wan Lixin Wan Pengbo Luan Pengbo Luan Jingru Liang Jingru Liang Weicai Cheng Weicai Cheng *
  • Yantaishan Hospital, Yantai, China

The final, formatted version of the article will be published soon.

    Objective: This study aimed to evaluate the impact of enteral nutrition with dietary fiber on patients undergoing laparoscopic colorectal cancer (CRC) surgery.Methods: 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.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 < 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 < 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.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.

    Keywords: colorectal cancer, Dietary Fiber, enteral nutrition support, Nutritional Status, machine learning

    Received: 09 Oct 2024; Accepted: 06 Jan 2025.

    Copyright: © 2025 Ji, Wan, 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) or licensor 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.

    * Correspondence: Weicai Cheng, Yantaishan Hospital, Yantai, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.