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

Xudong ZHANG; Shengli SUN; Hongchao WANG

doi:10.11959/j.issn.2096-0271.2019005

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

Topic:Big Data on Health Care | 更新时间：2024-06-03

- Intelligent diagnosis model and method of palpation imaging breast cancer based on data mining
- Big data research Vol. 5, Issue 1, Pages: 2019005(2019)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2019005
  CLC： TP18
- Online First：2019-01，
  
  Published：15 January 2019
- 稿件说明：
移动端阅览
Xudong ZHANG, Shengli SUN, Hongchao WANG. Intelligent diagnosis model and method of palpation imaging breast cancer based on data mining[J]. Big data research, 2019, 5(1): 2019005.
DOI：

Xudong ZHANG, Shengli SUN, Hongchao WANG. Intelligent diagnosis model and method of palpation imaging breast cancer based on data mining[J]. Big data research, 2019, 5(1): 2019005. DOI： 10.11959/j.issn.2096-0271.2019005.

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。

Abstract

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.

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references

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