An algorithm designed for improving diagnostic efficiency by setting multi-cutoff values of multiple tumor markers

Authors:
Qiang Su;Jinghua Shi;Ping Gu;Gang Huang;Yan Zhu
Affiliations:
School of Economics & Management, Tongji University, Shanghai 200092, China;Department of Industrial Engineering and Logistics Management, Shanghai Jiao Tong University, Shanghai 200240, China;Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China;Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China;School of Economics and Management, Tsinghua University, Beijing 100084, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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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.