A comparative study on machine classification model in lung cancer cases analysis
by Jing Li; Zhisheng Zhao; Yang Liu; Jie Li; Zhiwei Cheng; Xiaozheng Wang
International Journal of Applied Systemic Studies (IJASS), Vol. 7, No. 1/2/3, 2017

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.

Online publication date: Tue, 02-Jan-2018

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