基于BP-GamysBoost的乳腺癌诊断模型

针对乳腺癌数据存在的不平衡性问题，对标准的Adaboost算法进行改进，即首先引入BP神经网络，然后融合模拟退火遗传算法（SA-GA）较强的全局寻优能力和较快的收敛速度，最后通过权重的合理分配，提出BP-GamysBoost算法。同时为验证所提出的新算法BP-GamysBoost的合理性，从UCI机器学习知识库中获取WBCD数据库，比较BP-GamysBoost算法模型与BP模型、BP-GA模型、BP-Adaboost模型的稳定性、准确率、漏诊率、灵敏度等性能指标。最终结果表明，BP-GamysBoost模型在乳腺癌数据库中运行良好，并优于其他3种算法模型。

Breast Cancer Diagnosis Model Based on BP-GamysBoost

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.