Prediction of Pulmonary Function Parameters Based on a Combination Algorithm

Bioengineering

卷:9

Peng Wang¹  Xianxiang Chen¹  Yueqi Li¹  Zhan Zhao¹  Ruishi Zhou¹  Zhen Fang¹  Lidong Du¹  Xiuying Mou¹  Qingyuan Zhan²  Ting Yang² 

[1] Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), Beijing 100190, China;

[2] Department of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China;

关键词: combination algorithm;    support vector machines;    extreme gradient boosting;    one-dimensional convolutional neural network;    improved K-nearest neighbor;   

DOI  :  10.3390/bioengineering9040136

来源: DOAJ

【 摘 要 】

Objective: Pulmonary function parameters play a pivotal role in the assessment of respiratory diseases. However, the accuracy of the existing methods for the prediction of pulmonary function parameters is low. This study proposes a combination algorithm to improve the accuracy of pulmonary function parameter prediction. Methods: We first established a system to collect volumetric capnography and then processed the data with a combination algorithm to predict pulmonary function parameters. The algorithm consists of three main parts: a medical feature regression structure consisting of support vector machines (SVM) and extreme gradient boosting (XGBoost) algorithms, a sequence feature regression structure consisting of one-dimensional convolutional neural network (1D-CNN), and an error correction structure using improved K-nearest neighbor (KNN) algorithm. Results: The root mean square error (RMSE) of the pulmonary function parameters predicted by the combination algorithm was less than 0.39L and the R² was found to be greater than 0.85 through a ten-fold cross-validation experiment. Conclusion: Compared with the existing methods for predicting pulmonary function parameters, the present algorithm can achieve a higher accuracy rate. At the same time, this algorithm uses specific processing structures for different features, and the interpretability of the algorithm is ensured while mining the feature depth information.

【 授权许可】

Unknown