Machine Learning Analysis of Gut Microbiome Profiles as Predictors for Colorectal Cancer Risk: A Systematic Review and Meta-Analysis

Colorectal cancer (CRC) ranks as the third most deadly cancer worldwide, with high mortality rates in both developed and developing regions, largely due to late-stage diagnosis. This systematic review and meta-analysis, conducted in line with PRISMA guidelines, evaluates studies published between 2016 and 2024 that explore the potential of machine learning algorithms to predict colorectal cancer risk based on gut microbiome profiles. The inclusion criteria focused on studies employing machine learning methods to develop predictive models for CRC. A total of 12 studies were reviewed, each utilizing various machine learning approaches to enhance the accuracy of CRC diagnosis. The findings underscore the transformative role of machine learning in improving diagnostic precision for CRC, particularly through the analysis of gut microbiome profiles. These results demonstrate that machine learning-driven analyses hold great promise in advancing early detection and risk prediction of colorectal cancer, thus offering a valuable tool for improving patient outcomes.