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ENSEMBLE OF FLEXIBLE NEURAL TREES FOR BREAST CANCER DETECTION

Breast cancer is one of the major tumor related cause of death in women. Various artificial intelligence techniques have been used to improve the diagnoses procedures and to aid the physician’s efforts. In this paper, we summarize our study to detect breast cancer using a Flexible Neural Tree (FNT) and an ensemble of FNTs. For the FNT model, a tree-structure based evolutionary algorithm and the Particle Swarm Optimization (PSO) are used to find an optimal FNT. The performance of each approach is evaluated using the breast cancer data set. Simulation results show that the obtained FNT model has a fewer number of variables with reduced number of input features and without significant reduction in the detection accuracy. The overall accuracy could be improved by using an generalized ensemble method.