首页 / 院系成果 / 成果详情页

Development and validation of a predictive model for the diagnosis of solid solitary pulmonary nodules using data mining methods 期刊论文

编号：

a73c7fd2-4f19-41b9-b93a-c7432159e47c
作者：

Xiang, Yangwei^[1];Sun, Yifeng^[1];Liu, Yuan^[2];Han, Baohui^[3];Chen, Qunhui^[4];Ye, Xiaodan^[4];Zhu, Li^[4];Gao, Wen(高文)^[1,5]Fang, Wentao^[1];
语种：

英文
期刊：

JOURNAL OF THORACIC DISEASE ISSN：2072-1439 2019 年 11 卷 3 期 (950 - 958) ; MAR
收录：
关键词：

Lung cancer solitary pulmonary nodule (SPN) data mining
摘要：

Baokground: The purpose of this study is to develop a predictive model to accurately predict the malignancy of solid solitary pulmonary nodule (SPN) by data mining methods.
Methods: A training cohort of 388 consecutive patients with solid SPNs was used to develop a predictive model to evaluate the malignancy of solid SPNs. By using SPSS Modeler, we utilized logistic regression (LR), artificial neural network (ANN), k-nearest neighbor (KNN), random forest (RF), and support vector machines (SVM) classifiers to build predictive models. Another cohort of 200 consecutive patients with solid SPNs was used to verify the accuracy of the predictive model. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC).
Results: There was no significant difference in patients' characteristics between the training cohort and the validation cohort. The AUCs of LR, ANN, KNN, RF, and SVM models for the validation cohort were 0.874 +/- 0.0280 (P=0.605), 0.833 +/- 0.0351 (P=0.104), 0 .792 +/- 0 .0418 (P=0.014), 0 .775 +/- 0 .0400 (P=0.013), and 0.890 +/- 0.0323 (reference), respectively. The SVM algorithm had the highest AUC, and the best sensitivity (90.3%), specificity (80.4%), positive predictive value (93.9%), negative predictive value (71.2%) and accuracy (88.0%) for the validation cohort among the live models.
Conclusions: Data mining by SVM might be a useful auxiliary algorithm in predicting malignancy of solid SPNs.
推荐引用方式
GB/T 7714：

Xiang Yangwei,Sun Yifeng,Liu Yuan, et al. Development and validation of a predictive model for the diagnosis of solid solitary pulmonary nodules using data mining methods [J].JOURNAL OF THORACIC DISEASE,2019,11(3):950-958.
APA：

Xiang Yangwei,Sun Yifeng,Liu Yuan,Han Baohui,&Fang Wentao.(2019).Development and validation of a predictive model for the diagnosis of solid solitary pulmonary nodules using data mining methods .JOURNAL OF THORACIC DISEASE,11(3):950-958.
MLA：

Xiang Yangwei, et al. "Development and validation of a predictive model for the diagnosis of solid solitary pulmonary nodules using data mining methods" .JOURNAL OF THORACIC DISEASE 11,3(2019):950-958.

浏览次数：2  下载次数：0

分享到：

浏览次数：2

下载次数：0

打印次数：0