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A Comparative Study on Machine Classification Model in Lung Cancer Cases Analysis

  • Jing Li
  • Zhisheng Zhao
  • Yang Liu
  • Zhiwei Cheng
  • Xiaozheng Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 422)

Abstract

Due to the differences of machine classification models in the application of medical data, this paper selected different classification methods to study lung cancer data collected from HIS system with experimental analysis, applying the R language on decision tree algorithm, Bagging algorithm, Adaboost algorithm, SVM, KNN and neural network algorithm for lung cancer data analysis, in order to explore the advantages and disadvantages of each machine classification algorithm. The results confirmed that in lung cancer data research, Adaboost algorithm and neural network algorithm have relatively high accuracy, with a good diagnostic performance.

Keywords

Machine classification model Cross validation Adaboost algorithm Neural network 

Notes

Acknowledgements

1. Major Scientific Research Project in Higher School in Hebei Province (Grant No. ZD201310.-85), 2. Funding Project of Science and Technology Research and Development in Hebei North University (Grant No. ZD201301), 3. Major Projects in Hebei Food and Drug Administration (Grant No. ZD2015017), 4. Major Funding Project in Hebei Health Department (Grant No. ZL20140127), 5. Youth Funding Science and Technology Projects in Hebei Higher School (Grant No. QN2016192), with Hebei Province Population Health Information Engineering Technology Research Center.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Jing Li
    • 1
  • Zhisheng Zhao
    • 1
  • Yang Liu
    • 1
  • Zhiwei Cheng
    • 1
  • Xiaozheng Wang
    • 1
  1. 1.School of Information Science and EngineeringHebei North UniversityZhangjiakouChina

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