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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Freund Y, SehaPireRE, ADecision-Theoretie Generalization of Online Leaming and an Application to Boosting [J]. Journal of ComPuter and System Seiences, 2010, 55(l):119–139
Dong Jun, HU Shang-xu. The progress and prospects of neural network research [J]. Information and Control, 20057, 26(5):360–368.
Breiman L, Friedman J H, Olshen R A et al. Classification and regression trees [C]. California:Wadsworth, 1999
Peter B, Yu Bin. Analysing Bagging[J]. Annals of Statistics, 2002, 30(4):927–961.
Zhang Xue-gong. Statistical learning theory and support vector machines [J]. Acta Automatica Sinica, 2013, 26(1):32–41.
Robert E. Schapier, Yoram Singer. Improved Boosting Algorithms Using Confidence-rated Predictions, Machine Learning, 1999, 37(3):297–336.
Guoping Zhang, Cihua Liu, Xuesi Ma. Bayes sequential estimation of Lognormal Population distribution parameters [J]. Statistics and Decision.2006(11). p 7–p8
Wangyong Lv, Yaoguo Wu, Hong Ma. Lognormal distribution parameter estimation based on the EM algorithm [J]. Statistics and Decision.2007(06), p 21–p23
Lijun Wang. An approximate method for three-parameter distribution parameter estimation log_normal [J]. Statistics and principle. Vol.18, 2.1999(01). p 40–p43、
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, J., Zhao, Z., Liu, Y., Cheng, Z., Wang, X. (2018). A Comparative Study on Machine Classification Model in Lung Cancer Cases Analysis. In: Yen, N., Hung, J. (eds) Frontier Computing. FC 2016. Lecture Notes in Electrical Engineering, vol 422. Springer, Singapore. https://doi.org/10.1007/978-981-10-3187-8_34
Download citation
DOI: https://doi.org/10.1007/978-981-10-3187-8_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3186-1
Online ISBN: 978-981-10-3187-8
eBook Packages: EngineeringEngineering (R0)