title: Hybrid methods for automated diagnosis of breast tumors.
abstract: OBJECTIVE
  To design and analyze a new family of hybrid methods for the diagnosis of breast tumors using fine needle aspirates.
  
  
  METHODS
  We present a radically new approach to the design of diagnosis systems. In the new approach, a nonlinear classifier with high sensitivity but low specificity is hybridized with a linear classifier having low sensitivity but high specificity. Data from the Wisconsin Breast Cancer Database are used to evaluate, computationally, the performance of the hybrid classifiers.
  
  
  RESULTS
  The diagnosis scheme obtained by hybridizing the nonlinear classifier ellipsoidal multisurface method (EMSM) with the linear classifier proximal support vector machine (PSVM) was found to have a mean sensitivity of 97.36% and a mean specificity of 95.14% and was found to yield a 2.44% improvement in the reliability of positive diagnosis over that of EMSM at the expense of 0.4% degradation in the reliability of negative diagnosis, again compared to EMSM. At the 95% confidence level we can trust the hybrid method to be 96.19-98.53% correct in its malignant diagnosis of new tumors and 93.57-96.71% correct in its benign diagnosis.
  
  
  CONCLUSIONS
  Hybrid diagnosis schemes represent a significant paradigm shift and provide a promising new technique to improve the specificity of nonlinear classifiers without seriously affecting the high sensitivity of nonlinear classifiers.
authors.affiliation: School of Industrial Engineering, Purdue University, West Lafayette, Indiana 47907, USA.
authors.forename: Casey
authors.initials: C
authors.lastname: Diekman
authors.forename: Wei
authors.initials: W
authors.lastname: He
authors.forename: Nagabhushana
authors.initials: N
authors.lastname: Prabhu
authors.forename: Harvey
authors.initials: H
authors.lastname: Cramer
mesh_terms: D044963:Biopsy, Fine-Needle / Q000379:methods*; D001943:Breast Neoplasms / Q000473:pathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans