基于数据挖掘的触诊成像乳腺癌智能诊断模型和方法  

Intelligent diagnosis model and method of palpation imaging breast cancer based on data mining

作　　者：张旭东 孙圣力[1] 王洪超 ZHANG Xudong;SUN Shengli;WANG Hongchao(School of Software & Microelectronics,Peking University,Beijing 100089,China;Sinotau Pharmaceutical Group,Beijing 101300,China)

机构地区：[1]北京大学软件与微电子学院,北京100089 [2]北京先通康桥医药科技有限公司,北京101300

出　　处：《大数据》2019年第1期68-76,共9页Big Data Research

基　　金：江苏省自然科学基金资助项目(No.BK20151132)~~

摘　　要：为了辅助医护人员利用触诊成像技术判定乳腺癌,提出了触诊成像乳腺癌智能诊断模型和方法。采用乳腺癌早期筛查及风险评估的临床数据,以触诊成像诊断结果为对比数据,通过决策树等机器学习算法以及投票法,对乳腺肿瘤的良恶性质进行判定。使用SMOTE算法对数据进行处理,建立了诊断模型和方法,自动完成对乳腺肿瘤性质的诊断。实验结果表明,乳腺癌正确筛查的准确性达到98%,提出的方法具有很好的应用价值。In order to assist the medical staff to diagnose breast cancer more effectively by palpation imaging technology, intelligent diagnosis model and method of palpation imaging breast cancer were established. Based on clinical data for early breast cancer screening and risk assessment, machine learning algorithms of decision tree, neural network, SVM, logistic regression, Bayesian network and five voting methods were adopted to distinguish breast tumor, or positive and negative outcome in algorithms. The positive sample data was incremented by the SMOTE algorithm, intelligent diagnosis model was established, and model can automatically diagnose breast tumors. Palpation imaging intelligent diagnosis model of breast cancer correctly screens all cases of breast cancer confirmed by pathology, and the accuracy of the model is as high as 98%. The intelligent diagnosis model is excellent as a screening modality for the detection of breast cancer.

关 键 词：智能诊断 临床数据 机器学习 SMOTE算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]