Prediction of 30-day risk of acute exacerbation of readmission in elderly patients with COPD based on support vector machine model
Abstract Background Acute exacerbation of chronic obstructive pulmonary disease (COPD) is an important event in the process of disease management. Early identification of high-risk groups for readmission and appropriate measures can avoid readmission in some groups, but there is still a lack of spec...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2022-07-01
|
| Series: | BMC Pulmonary Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12890-022-02085-w |
| _version_ | 1818492383758647296 |
|---|---|
| author | Rui Zhang Hongyan Lu Yan Chang Xiaona Zhang Jie Zhao Xindan Li |
| author_facet | Rui Zhang Hongyan Lu Yan Chang Xiaona Zhang Jie Zhao Xindan Li |
| author_sort | Rui Zhang |
| collection | DOAJ |
| description | Abstract Background Acute exacerbation of chronic obstructive pulmonary disease (COPD) is an important event in the process of disease management. Early identification of high-risk groups for readmission and appropriate measures can avoid readmission in some groups, but there is still a lack of specific prediction tools. The predictive performance of the model built by support vector machine (SVM) has been gradually recognized by the medical field. This study intends to predict the risk of acute exacerbation of readmission in elderly COPD patients within 30 days by SVM, in order to provide scientific basis for screening and prevention of high-risk patients with readmission. Methods A total of 1058 elderly COPD patients from the respiratory department of 13 general hospitals in Ningxia region of China from April 2019 to August 2020 were selected as the study subjects by convenience sampling method, and were followed up to 30 days after discharge. Discuss the influencing factors of patient readmission, and built four kernel function models of Linear-SVM, Polynomial-SVM, Sigmoid-SVM and RBF-SVM based on the influencing factors. According to the ratio of training set and test set 7:3, they are divided into training set samples and test set samples, Analyze and Compare the prediction efficiency of the four kernel functions by the precision, recall, accuracy, F1 index and area under the ROC curve (AUC). Results Education level, smoking status, coronary heart disease, hospitalization times of acute exacerbation of COPD in the past 1 year, whether long-term home oxygen therapy, whether regular medication, nutritional status and seasonal factors were the influencing factors for readmission. The training set shows that Linear-SVM, Polynomial-SVM, Sigmoid-SVM and RBF-SVM precision respectively were 69.89, 78.07, 79.37 and 84.21; Recall respectively were 50.78, 69.53, 78.74 and 88.19; Accuracy respectively were 83.92, 88.69, 90.81 and 93.82; F1 index respectively were 0.59, 0.74, 0.79 and 0.86; AUC were 0.722, 0.819, 0.866 and 0.918. Test set precision respectively were86.36, 87.50, 80.77 and 88.24; Recall respectively were51.35, 75.68, 56.76 and 81.08; Accuracy respectively were 85.11, 90.78, 85.11 and 92.20; F1 index respectively were 0.64, 0.81, 0.67 and 0.85; AUC respectively were 0.742, 0.858, 0.759 and 0.885. Conclusions This study found the factors that may affect readmission, and the SVM model constructed based on the above factors achieved a certain predictive effect on the risk of readmission, which has certain reference value. |
| first_indexed | 2024-12-10T17:42:19Z |
| format | Article |
| id | doaj.art-7803eb85b3ba4425a7a9c4b6213390a1 |
| institution | Directory Open Access Journal |
| issn | 1471-2466 |
| language | English |
| last_indexed | 2024-12-10T17:42:19Z |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pulmonary Medicine |
| spelling | doaj.art-7803eb85b3ba4425a7a9c4b6213390a12022-12-22T01:39:20ZengBMCBMC Pulmonary Medicine1471-24662022-07-0122111010.1186/s12890-022-02085-wPrediction of 30-day risk of acute exacerbation of readmission in elderly patients with COPD based on support vector machine modelRui Zhang0Hongyan Lu1Yan Chang2Xiaona Zhang3Jie Zhao4Xindan Li5Department of Nursing, The General Hospital of Ningxia Medical UniversityDepartment of Nursing, The General Hospital of Ningxia Medical UniversityDepartment of Nursing, The General Hospital of Ningxia Medical UniversityDepartment of Nursing, The General Hospital of Ningxia Medical UniversityDepartment of Nursing, The General Hospital of Ningxia Medical UniversityDepartment of Nursing, The General Hospital of Ningxia Medical UniversityAbstract Background Acute exacerbation of chronic obstructive pulmonary disease (COPD) is an important event in the process of disease management. Early identification of high-risk groups for readmission and appropriate measures can avoid readmission in some groups, but there is still a lack of specific prediction tools. The predictive performance of the model built by support vector machine (SVM) has been gradually recognized by the medical field. This study intends to predict the risk of acute exacerbation of readmission in elderly COPD patients within 30 days by SVM, in order to provide scientific basis for screening and prevention of high-risk patients with readmission. Methods A total of 1058 elderly COPD patients from the respiratory department of 13 general hospitals in Ningxia region of China from April 2019 to August 2020 were selected as the study subjects by convenience sampling method, and were followed up to 30 days after discharge. Discuss the influencing factors of patient readmission, and built four kernel function models of Linear-SVM, Polynomial-SVM, Sigmoid-SVM and RBF-SVM based on the influencing factors. According to the ratio of training set and test set 7:3, they are divided into training set samples and test set samples, Analyze and Compare the prediction efficiency of the four kernel functions by the precision, recall, accuracy, F1 index and area under the ROC curve (AUC). Results Education level, smoking status, coronary heart disease, hospitalization times of acute exacerbation of COPD in the past 1 year, whether long-term home oxygen therapy, whether regular medication, nutritional status and seasonal factors were the influencing factors for readmission. The training set shows that Linear-SVM, Polynomial-SVM, Sigmoid-SVM and RBF-SVM precision respectively were 69.89, 78.07, 79.37 and 84.21; Recall respectively were 50.78, 69.53, 78.74 and 88.19; Accuracy respectively were 83.92, 88.69, 90.81 and 93.82; F1 index respectively were 0.59, 0.74, 0.79 and 0.86; AUC were 0.722, 0.819, 0.866 and 0.918. Test set precision respectively were86.36, 87.50, 80.77 and 88.24; Recall respectively were51.35, 75.68, 56.76 and 81.08; Accuracy respectively were 85.11, 90.78, 85.11 and 92.20; F1 index respectively were 0.64, 0.81, 0.67 and 0.85; AUC respectively were 0.742, 0.858, 0.759 and 0.885. Conclusions This study found the factors that may affect readmission, and the SVM model constructed based on the above factors achieved a certain predictive effect on the risk of readmission, which has certain reference value.https://doi.org/10.1186/s12890-022-02085-wOld ageCOPDSVM |
| spellingShingle | Rui Zhang Hongyan Lu Yan Chang Xiaona Zhang Jie Zhao Xindan Li Prediction of 30-day risk of acute exacerbation of readmission in elderly patients with COPD based on support vector machine model BMC Pulmonary Medicine Old age COPD SVM |
| title | Prediction of 30-day risk of acute exacerbation of readmission in elderly patients with COPD based on support vector machine model |
| title_full | Prediction of 30-day risk of acute exacerbation of readmission in elderly patients with COPD based on support vector machine model |
| title_fullStr | Prediction of 30-day risk of acute exacerbation of readmission in elderly patients with COPD based on support vector machine model |
| title_full_unstemmed | Prediction of 30-day risk of acute exacerbation of readmission in elderly patients with COPD based on support vector machine model |
| title_short | Prediction of 30-day risk of acute exacerbation of readmission in elderly patients with COPD based on support vector machine model |
| title_sort | prediction of 30 day risk of acute exacerbation of readmission in elderly patients with copd based on support vector machine model |
| topic | Old age COPD SVM |
| url | https://doi.org/10.1186/s12890-022-02085-w |
| work_keys_str_mv | AT ruizhang predictionof30dayriskofacuteexacerbationofreadmissioninelderlypatientswithcopdbasedonsupportvectormachinemodel AT hongyanlu predictionof30dayriskofacuteexacerbationofreadmissioninelderlypatientswithcopdbasedonsupportvectormachinemodel AT yanchang predictionof30dayriskofacuteexacerbationofreadmissioninelderlypatientswithcopdbasedonsupportvectormachinemodel AT xiaonazhang predictionof30dayriskofacuteexacerbationofreadmissioninelderlypatientswithcopdbasedonsupportvectormachinemodel AT jiezhao predictionof30dayriskofacuteexacerbationofreadmissioninelderlypatientswithcopdbasedonsupportvectormachinemodel AT xindanli predictionof30dayriskofacuteexacerbationofreadmissioninelderlypatientswithcopdbasedonsupportvectormachinemodel |