ArticlePloS one2025
A practical approach for colorectal cancer diagnosis based on machine learning.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It carries an expression of concern. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Unraveling gene interaction networks in colorectal cancer and inflammatory bowel disease via a novel hybrid radial basis function network.Scientific reports · 2026Article
- Review
- Expression of Concern: A practical approach for colorectal cancer diagnosis based on machine learning.PloS one · 2026Article
- Hybrid deep learning with attention fusion for enhanced colon cancer detection.Scientific reports · 2025Article
- Photoacoustic-Integrated Multimodal Approach for Colorectal Cancer Diagnosis.ACS biomaterials science & engineering · 2025Review
- Intelligent tongue and facial image analysis for noninvasive prediction of glucolipid metabolic disorders.Digital healthArticle
Corrections and comments
- Expression of concern
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.