Performance Analysis of Evolutionary Computation (EC)/Adaptive Boosting (AB) Hybrids for Breast Cancer Classification

摘要

A new neural network technology was developed to improve the diagnosis of breast cancer using mammogram findings. The paradigm, Adaptive Boosting (AB), focuses on finding weak learning algorithm(s) that initially need to provide slightly better than "random" performance (i.e., approximately 55%) when processing a mammogram training set. By successive development of additional architectures (using the mammogram training set), the adaptive boosting process improves performance of the basic Evolutionary Programming derived neural network architectures. The results of these several EP-derived hybrid architectures are then intelligently combined and tested using a similar validation mammogram data set. Because we were particularly interested in maximizing positive predictive value (PPV) and specificity at high sensitivity levels, the error-minimization selection method used by the EP component was replaced with a selection method favoring those solutions with the best predictive value. Preliminary results using the new fitness function were then compared with optimized results using the original fitness function. Preliminary results using the Duke University mammogram database of 500 biopsy proven samples show that this PPV-based hybrid was able to achieve (under statistical 5-fold cross-validation), on average, PPV and specificity results comparable to the best results obtained using the error-minimization hybrid.