Using Evolutionary Computation to Develop Neural Network Breast Cancer Benign/Malignant Classification Models

摘要

An Evolutionary Programming (EP) benign/malignant breast cancer neural network classification model was developed and investigated which predicts the outcome of mammography-induced breast biopsy. By combining input variables consisting of mammography lesion descriptors and patient history data, the EP derived neural network predicted whether the lesion was benign or malignant. Computer Aided Diagnostic (CAD) tools such as this model may aid in reducing the number of unnecessary biopsies and thus the cost of mammography screening for breast cancer. The EP process was optimized using a data set of 500 biopsy-proven cases from Duke University Medical Center. Results showed that the best EP derived neural network classifier provided an ROC A_z index of 0.843 +-0.053 when averaging performance over 5-fold cross-validation statistical experiments.