Construction of diagnostic models with machine-learning algorithms for colorectal cancer based on clinical laboratory parameters.

Background: Colonoscopy remains the predominant diagnostic modality for colorectal cancer (CRC), as the diagnostic performance of tumor markers in alone, particularly in the early stages of the disease, is limited. This study sought to develop a diagnostic model for CRC that integrated various laboratory parameters.

Methods: One hundred patients with CRC were assigned to an experimental group while 114 with benign colorectal diseases and 101 healthy individuals were assigned to a control group.