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Predicting malignancy of pulmonary ground-glass nodules and their invasiveness by random forest

   
@article{JTD18552,
	author = {Xueyan Mei and Rui Wang and Wenjia Yang and Fangfei Qian and Xiaodan Ye and Li Zhu and Qunhui Chen and Baohui Han and Timothy Deyer and Jingyi Zeng and Xiaomeng Dong and Wen Gao and Wentao Fang},
	title = {Predicting malignancy of pulmonary ground-glass nodules and their invasiveness by random forest},
	journal = {Journal of Thoracic Disease},
	volume = {10},
	number = {1},
	year = {2018},
	keywords = {},
	abstract = {Background: The purpose of this study was to develop a predictive model that could accurately predict the malignancy of the pulmonary ground-glass nodules (GGNs) and the invasiveness of the malignant GGNs. 
Methods: The authors built two binary classification models that could predict the malignancy of the pulmonary GGNs and the invasiveness of the malignant GGNs 
Results: Results of our developed model showed random forest could achieve 95.1% accuracy to predict the malignancy of GGNs and 83.0% accuracy to predict the invasiveness of the malignant GGNs. 
Conclusions: The malignancy and invasiveness of pulmonary GGNs could be predicted by random forest.},
	issn = {2077-6624},	url = {https://jtd.amegroups.org/article/view/18552}
}