Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (5): 56-59.doi: 10.16180/j.cnki.issn1007-7820.2022.05.009

Previous Articles     Next Articles

Tumor Intelligent Auxiliary Diagnosis Method Based on Machine Learning

CHENG Shunda1,CHENG Ying2,SUN Shijiang1   

  1. 1. Hebei Hospital of Traditional Chinese Medicine,Shijiazhuang 050000,China
    2. Statistical Information Center,Hebei Health Commission, Shijiazhuang 050051,China
  • Received:2020-12-04 Online:2022-05-25 Published:2022-05-27
  • Supported by:
    Project of National Clinical Research Base of Traditional Chinese Medicine (Science and Technology Letter of Chinese Medicine Office [2018]18);Project of Scientific Research Plan of Hebei Administration of Traditional Chinese Medicine in 2020(KTY2020104)


In the field of tumor diagnosis, the artificial intelligence-assisted diagnosis system can accurately distinguish and diagnose tumor attributes and malignant tumor stages, thereby prolonging the survival time of patients. In this study, taking breast tumor as a case, in view of the over-fitting problem caused by the excessive amount of data in the feature extraction process, a supervised learning artificial intelligence-assisted diagnosis model is proposed. When extracting features, hierarchical clustering analysis is introduced to perform effective feature reduction, and the classified feature data is used as the feature input of the artificial neural network model to achieve effective training of the classifier. The experimental results show that compared with other algorithm, the accuracy and AUC value of the proposed algorithm are improved, indicating that the model can not only solve the over-fitting problem caused by the description of massive feature regions, but also enhance the artificial intelligence-assisted diagnosis, thereby completing the mammography target breast tumor high-precision distinction.

Key words: machine learning, malignant tumor, auxiliary diagnosis, feature selection, feature dimension reduction, classifier, hierarchical clustering, neural network

CLC Number: 

  • TP391