Expert system for breast cancer diagnosis using ensemble approach
NIT Raipur, Rekh Ram Janghel, Anupam Shukla · CSVTU International Journal of Biotechnology Bioinformatics and Biomedical · 2016
Development of efficient, prompt and robust systems that are intelligent enough to replace/reduce human supervision in medical diagnosis is one of the primary objectives that have driven advancements in research for long. One area where this need for intelligent automation has been most acutely felt is related to the diagnosis of breast cancer in women. Mammography is the most widely used test for screening and early diagnosis of breast cancer. However, it is error-prone and hence cannot be used reliably for effective diagnosis of the said disease. In this paper, we describe how the above said problem could be efficiently solved through Ensemble Approach. The use of the approach has bestowed the expert system with significantly simple and swift learning, smaller requirement for storage space during classification, faster classification with added possibility of incremental learning. The system was comparatively evaluated using different ensemble integration methods for breast cancer diagnosis namely weighted averaging, product, minimum, maximum, poling and fuzzy integration and different neural network approaches including MLP neural network, Radial Basis Function Network, Learning Vector Quantization and Recurrent Neural Network. Detailed experimental analysis with the system shows that the best performance in terms of accuracy and specificity measures is achieved while used maximum integration technique with Radial Bass Function Network while finest performance in terms of sensitivity is achieved when MLP neural network with Minimum integration is used.