title: Model-independent evaluation of tumor markers and a logistic-tree approach to diagnostic decision support.
abstract: Sensitivity and specificity of using individual tumor markers hardly meet the clinical requirement. This challenge gave rise to many efforts, e.g., combing multiple tumor markers and employing machine learning algorithms. However, results from different studies are often inconsistent, which are partially attributed to the use of different evaluation criteria. Also, the wide use of model-dependent validation leads to high possibility of data overfitting when complex models are used for diagnosis. We propose two model-independent criteria, namely, area under the curve (AUC) and Relief to evaluate the diagnostic values of individual and multiple tumor markers, respectively. For diagnostic decision support, we propose the use of logistic-tree which combines decision tree and logistic regression. Application on a colorectal cancer dataset shows that the proposed evaluation criteria produce results that are consistent with current knowledge. Furthermore, the simple and highly interpretable logistic-tree has diagnostic performance that is competitive with other complex models.
authors.affiliation: Department of Mechanical Engineering, University of Cincinnati Cincinnati, OH, USA.
authors.forename: Weizeng
authors.initials: W
authors.lastname: Ni
authors.affiliation: Department of Mechanical Engineering, University of Cincinnati Cincinnati, OH, USA.
authors.forename: Samuel H
authors.initials: SH
authors.lastname: Huang
authors.affiliation: School of Economics & Management, Tongji University Shanghai, P. R. China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Su
authors.affiliation: Department of Industrial Engineering and Logistics Management, Shanghai Jiaotong University, Shanghai, P. R. China.
authors.forename: Jinghua
authors.initials: J
authors.lastname: Shi
mesh_terms: D000704:Analysis of Variance; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000097:blood*; D016208:Databases, Factual; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D009369:Neoplasms / Q000097:blood* / Q000175:diagnosis*; D012372:ROC Curve
chemical_list: D014408:Biomarkers, Tumor
keywords: cancer diagnosis; decision support; machine learning; tumor marker