Application of a new evolutionary programming/adaptive boosting hybrid to breast cancer diagnosis

A new evolutionary programming/adaptive boosting (EP/AB) neural network hybrid was investigated to measure the hybrid performance improvement as obtained when using an EP-only derived neural network as a baseline. By combining input variables consisting of mammography lesion descriptors and patient history data, the hybrid predicted whether the lesion was benign or malignant, which may aid in reducing the number of unnecessary biopsies and thus the cost of mammography screening of breast cancer. The EP process as well as the hybrid was optimized using a data set of 500 biopsy-proven cases from Duke University Medical Center (USA). Results showed that the hybrid provided a 15-20% classification performance improvement as measured by the ROC Az index when compared to a non-optimized EP derived architecture.