Abstract
Currently, tumor markers have been effectively applied for colorectal cancer (CRC) diagnosis. In order to decrease the information loss caused by single cutoff value and improve diagnosis efficiency (DE), we explore the integrative application of multiple tumor markers with multiple cutoff values systematically by developing an optimization algorithm named MVMTM. The effectiveness of the MVMTM is experimentally studied based on a real medical dataset. With MVMTM, the united use of three tumor markers can enhance DE from 0.78 to 0.86. Furthermore, MVMTM has been proved to be better than other baseline machine learning algorithms significantly.
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