基于BP＿Adaboost模型的乳腺癌诊断预测方法研究

乳腺癌是全球女性发病率位居首位的恶性肿瘤，研究基于神经网络模型的乳腺癌诊断预测方法的目的是将临床与机器学习相结合，有助于医疗工作者更加快速准确地判断出患病与否，同时解决现有模型中存在的过拟合以及漏诊率和误诊率过高的问题，并提高预测模型的准确率。本文采用加州大学欧文分校(UCI)数据集，共669个样本，其中包含357个良性样本和212个恶性肿瘤样本，共计10个特征训练预测模型。将10个神经网络模型采用Adaboost方法相结合，即通过Adaboost算法组合多个弱分类器从而形成一个强分类器，最终输出一个具有更高准确率、有较强的自学习能力、自适应能力且泛化性能优良的集成预测模型。结论表明，该模型的预测准确率达到98.5507%，同时准确率(AUC)为0.9966，说明所建模型区分度较好，可以反映模型的诊断价值，且非常稳定，具有非常好的验证效果，为临床应用提供进一步的技术支持和保障。

Diagnosis and prediction of breast cancer based on BP_Adaboost model

Breast cancer is the first malignant tumor in women worldwide. Studying on breast cancer diagnosis and prediction methods based on neural network models is to combine clinical and machine learning to help medical workers more quickly and accurately determine the disease or not, and solve the problems of over-fitting, missed diagnosis rate and high misdiagnosis rate in existing models, and improve the accuracy of prediction models. The University of California Irvine(UCI) data set contains 669 samples, including 357 benign samples and 212 malignant tumor samples, a total of 10 features to train the prediction model. The 10 neural network models are combined through Adaboost method, that is, multiple weak classifiers are combined by Adaboost algorithm to form a strong classifier. The final output is an integrated prediction model with higher accuracy, stronger self-learning ability, adaptive ability and excellent generalization performance. The conclusion shows that the prediction accuracy of the model is 98.550 7%, and the Accuracy(AUC) is 0.996 6, which indicates that the established model is very stable, and has good discrimination and good verification effects. It provides further technical support and guarantee for clinical application.