title: Logistic Regression-Based Trichotomous Classification Tree and Its Application in Medical Diagnosis.
abstract: The classification tree is a valuable methodology for predictive modeling and data mining. However, the current existing classification trees ignore the fact that there might be a subset of individuals who cannot be well classified based on the information of the given set of predictor variables and who might be classified with a higher error rate; most of the current existing classification trees do not use the combination of variables in each step. An algorithm of a logistic regression-based trichotomous classification tree (LRTCT) is proposed that employs the trichotomous tree structure and the linear combination of predictor variables in the recursive partitioning process. Compared with the widely used classification and regression tree through the applications on a series of simulated data and 2 real data sets, the LRTCT performed better in several aspects and does not require excessive complicated calculations.
authors.affiliation: Department of Mathematics, College of Mathematics and Informatics, South China Agricultural University, Guangzhou, Guangdong, China (YZ)
authors.forename: Yanke
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China (JF)
authors.forename: Jiqian
authors.initials: J
authors.lastname: Fang
mesh_terms: D000465:Algorithms; D000066491:Clinical Decision-Making / Q000379:methods*; D003663:Decision Trees; D006801:Humans; D016015:Logistic Models
keywords: classification and regression tree (CART); data mining; decision tree; logistic regression (LR); logistic regression–based trichotomous classification tree (LRTCT)