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A comparative study on machine classification model in lung cancer cases analysis

A comparative study on machine classification model in lung cancer cases analysis 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 plenty of experiment and analysis, applying the R language on decision tree algorithm, bagging algorithm, Adaboost algorithm, conditions decision tree, random forests, naive Bayes, 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, naive Bayes, Adaboost algorithm and neural network algorithm have relatively high accuracy, with a good diagnostic performance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Applied Systemic Studies Inderscience Publishers

A comparative study on machine classification model in lung cancer cases analysis

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1751-0589
eISSN
1751-0597
DOI
10.1504/IJASS.2017.088906
Publisher site
See Article on Publisher Site

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 plenty of experiment and analysis, applying the R language on decision tree algorithm, bagging algorithm, Adaboost algorithm, conditions decision tree, random forests, naive Bayes, 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, naive Bayes, Adaboost algorithm and neural network algorithm have relatively high accuracy, with a good diagnostic performance.

Journal

International Journal of Applied Systemic StudiesInderscience Publishers

Published: Jan 1, 2017

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