New results in breast cancer classification obtained from an evolutionary computation/adaptive boosting hybrid using mammogram and history data

Abstract

A new neural network technology was developed to improve the diagnosis of breast cancer using mammogram findings. The paradigm, adaptive boosting (AB), uses a markedly different theory in solving the computational intelligence (CI) problem. AB, a new machine learning paradigm, 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. Optimization, focused on improving specificity and positive predictive value at very high sensitivities, with an analysis of the performance of the hybrid would be most meaningful. Using the DUKE mammogram database of 500 biopsy proven samples, this hybrid, on average, was able to achieve (under statistical 5-fold cross-validation) a specificity of 48.3% and a positive predictive value (PPV) of 51.8% while maintaining 100% sensitivity. At 97% sensitivity, a specificity of 56.6% and a PPV of 55.8% were obtained.