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