Title :
New results in breast cancer classification obtained from an evolutionary computation/adaptive boosting hybrid using mammogram and history data
Author :
Land, Walker H., Jr. ; Masters, Timothy ; Lo, Joseph Y. ; McKee, Daniel W. ; Anderson, Frances R.
Author_Institution :
Binghamton Univ., NY, USA
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
Keywords :
adaptive systems; cancer; evolutionary computation; history; learning (artificial intelligence); mammography; medical diagnostic computing; neural nets; pattern classification; DUKE mammogram database; biopsy proven samples; breast cancer classification; breast cancer diagnosis; evolutionary computation/adaptive boosting hybrid; evolutionary programming derived neural network architectures; history data; machine learning paradigm; mammogram training set; neural network technology; optimization; positive predictive value; specificity; validation mammogram data set; weak learning algorithms; Boosting; Breast cancer; Competitive intelligence; Computational intelligence; Computer architecture; Genetic programming; Machine learning; Machine learning algorithms; Neural networks; Testing;
Conference_Titel :
Soft Computing in Industrial Applications, 2001. SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on
Conference_Location :
Blacksburg, VA
Print_ISBN :
0-7803-7154-2
DOI :
10.1109/SMCIA.2001.936727