title: Subpopulation-specific confidence designation for more informative biomedical classification.
abstract: OBJECTIVE
  Although classification algorithms are promising tools to support clinical diagnosis and treatment of disease, the usual implicit assumption underlying these algorithms, that all patients are homogeneous with respect to characteristics of interest, is unsatisfactory. The objective here is to exploit the population heterogeneity reflected by characteristics that may not be apparent and thus not controlled, in order to differentiate levels of classification accuracy between subpopulations and further the goal of tailoring therapies on an individual basis.
  
  
  METHODS
  A new subpopulation-based confidence approach is developed in the context of a selective voting algorithm defined by an ensemble of convex-hull classifiers. Populations of training samples are divided into three subpopulations that are internally homogeneous, with different levels of predictivity. Two different distance measures are used to cluster training samples into subpopulations and assign test samples to these subpopulations.
  
  
  RESULTS
  Validation of the new approach's levels of confidence of classification is carried out using six publicly available datasets. Our approach demonstrates a positive correspondence between the predictivity designations derived from training samples and the classification accuracy of test samples. The average difference between highest- and lowest-confidence accuracies for the six datasets is 17.8%, with a minimum of 11.3% and a maximum of 24.1%.
  
  
  CONCLUSIONS
  The classification accuracy increases as the designated confidence increases.
authors.affiliation: Department of Applied Mathematics and Computer Science, Philander Smith College, 900 W. Daisy L. Gatson Bates Dr., Little Rock, AR 72202, USA.
authors.forename: Chuanlei
authors.initials: C
authors.lastname: Zhang
authors.forename: Ralph L
authors.initials: RL
authors.lastname: Kodell
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D005820:Genetic Testing / Q000379:methods; D014644:Genetic Variation; D005838:Genotype; D006801:Humans; D010363:Pattern Recognition, Automated; D010641:Phenotype; D057285:Precision Medicine / Q000379:methods*; D011237:Predictive Value of Tests; D011379:Prognosis; D015203:Reproducibility of Results; D013813:Therapy, Computer-Assisted
keywords: Cross-validation; Genomic prediction; Individualized therapy; Population heterogeneity