Stacked random forest model for colorectal cancer detection using complete blood counts.

ABSTRACT:

Background

In China, adherence to screening colonoscopy among eligible individuals remains suboptimal, primarily due to cost concerns and potential adverse effects. A machine learning model utilizing complete blood count (CBC) data could help prioritize colonoscopy referrals and improve screening participation.

Method

This multicenter study included participants who underwent CBC testing within three months before colonoscopy. CBC data were classified into three types (A, B, and C) based on hematology analyzer capabilities, with Type C excluded from analysis. Using Types A and B, we developed a stacking machine learning model incorporating 24 CBC features and 5 combined CBC components to predict colorectal cancer (CRC). Model performance was evaluated using the area under th

SUBMITTER: Luo J 

PROVIDER: S-EPMC12317218 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature