10 December 2021>: Clinical Research
Development and Validation of a Random Forest Risk Prediction Pneumothorax Model in Percutaneous Transthoracic Needle Biopsy
Hong Lin Wu 1ABCDEF , Gao Wu Yan 2ADF , Li Cheng Lei 3BC , Yong Du 4AD , Xiang Ke Niu 1DF , Tao Peng 1FG*DOI: 10.12659/MSM.932137
Med Sci Monit 2021; 27:e932137
Table 3 Prediction performance of models based on 15 risk factors.
Models | Development set | Verification set | ||||||
---|---|---|---|---|---|---|---|---|
LR | SVM | DT | RF | LR | SVM | DT | RF | |
Sensitivity | 0.701 | 0.781 | 0.759 | 0.825 | 0.581 | 0.791 | 0.884 | 0.930 |
Specificity | 0.500 | 0.574 | 0.676 | 0.806 | 0.667 | 0.611 | 0.556 | 0.759 |
Accuracy | 0.612 | 0.690 | 0.722 | 0.816 | 0.629 | 0.691 | 0.701 | 0.835 |
PPV | 0.640 | 0.699 | 0.748 | 0.843 | 0.581 | 0.618 | 0.613 | 0.755 |
NPV | 0.568 | 0.674 | 0.689 | 0.784 | 0.667 | 0.786 | 0.857 | 0.932 |
PPV – positive predictive value; NPV – negative predictive value; LR – logistic regression; SVM – support vector machine; DT – decision tree; RF – random forest. |