Breast Cancer Diagnosis Model Based on BP-GamysBoost

Abstract

Abstract: In view of the problem of unbalance of the breast cancer data, the standard Adaboost algorithm is improved. First, BP neural network is introduced, then the strong global optimization ability and fast convergence speed of simulated annealing genetic algorithm (SA-GA) are fused, and finally the weight is allocated reasonably to propose the BP-GamysBoost algorithm. At the same time, in order to verify the rationality of the proposed new algorithm BP-GamysBoost, the WBCD database is obtained from the UCI machine learning knowledge base, and the performance indexes such as stability, accuracy, missed diagnosis rate and sensitivity of BP-GamysBoost algorithm model are compared with BP model, BP-GA model and BP-Adaboost model. The results show that the BP-GamysBoost model works well in the breast cancer database and is superior to the other three algorithm models.

Key words: Adaboost algorithm, simulated annealing genetic algorithm, BP neural network, BP-GamysBoost model

LIU Jun, PENG Hui-xian, HUANG Bin, Tony SHAY. Breast Cancer Diagnosis Model Based on BP-GamysBoost[J]. Computer and Modernization, 2021, 0(04): 8-14.

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