机器学习技术在胸癌诊断中的应用

为了提高胸癌诊断的识别精度,提出了应用机器学习方法建立胸癌诊断模型。其中描述细胞特征的参量作为模型的输入,细胞的类别对应模型的输出。选取三种机器学习方法作为建立模型的训练算法,分别为反向传播(Back Propagation,BP)神经网络、学习矢量量化网络(Learning Vector Quantity,LVQ)和支持向量机(Support Vector Machine,SVM)。仿真结果显示三种机器学习方法所见的诊断模型均具有较高的识别率(BP:97.28%,LVQ:98.06%,SVM:98.45%),可作为有效地识别方法用于其他医学诊断研究。

Breast Cancer Diagnosis using Machine Learning Technique

In order to improve the diagnosis accuracy, machine learning method was proposed to construct the breast cancer diagnosis model. The parameters of cell feature are the inputs of model and the class of diagnosed cell is the output. Three machine learning methods are chosen as training algorithm, including BP neural network, learning vector quantity network and support vector machine. Simulation results show that three methods have high identification ability (BP:97.28%, LVQ: 98.06%, SVM: 98.45%) and can be applied to other medicine research as effective method.