Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning
Abstract Background Respiratory diseases and Cardiovascular Diseases (CVD) often coexist, with airflow obstruction (AO) severity closely linked to CVD incidence and mortality. As both conditions rise, early identification and intervention in risk populations are crucial. However, current CVD risk mo...
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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-02885-0 |
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| author | Xiyu Cao Jianli Ma Xiaoyi He Yufei Liu Yang Yang Yaqi Wang Chuantao Zhang |
| author_facet | Xiyu Cao Jianli Ma Xiaoyi He Yufei Liu Yang Yang Yaqi Wang Chuantao Zhang |
| author_sort | Xiyu Cao |
| collection | DOAJ |
| description | Abstract Background Respiratory diseases and Cardiovascular Diseases (CVD) often coexist, with airflow obstruction (AO) severity closely linked to CVD incidence and mortality. As both conditions rise, early identification and intervention in risk populations are crucial. However, current CVD risk models inadequately consider AO as an independent risk factor. Therefore, developing an accurate risk prediction model can help identify and intervene early. Methods This study used the National Health and Nutrition Examination Survey (NHANES) III (1988–1994) and NHANES 2007–2012 datasets. Inclusion criteria were participants aged over 40 with complete AO and CVD data; exclusions were those with missing key data. Analysis included 12 variables: age, gender, race, PIR, education, smoking, alcohol, BMI, hyperlipidemia, hypertension, diabetes, and AO. Logistic regression analyzed the association between AO and CVD, with sensitivity and subgroup analyses. Six ML models predicted CVD risk for the general population, using AO as a predictor. RandomizedSearchCV with 5-fold cross-validation was used for hyperparameter optimization. Models were evaluated by AUC, accuracy, precision, recall, F1 score, and Brier score, with the SHapley Additive exPlanations (SHAP) enhancing explainability. A separate ML model was built for the subpopulation with AO, evaluated similarly. Results The cross-sectional analysis showed that there was a significant positive correlation between AO occurrence and CVD prevalence, indicating that AO is an important risk factor for CVD (all P < 0.05). For the general population, the XGBoost model was selected as the optimal model for predicting CVD risk (AUC = 0.7508, AP = 0.3186). The top three features in terms of importance were age, hypertension, and PIR. For the subpopulation with airflow obstruction, the XGBoost model was also selected as the optimal model for predicting CVD risk (AUC = 0.6645, AP = 0.3545). SHAP shows that education level has the greatest impact on predicting CVD risk, followed by gender and race. Conclusion AO correlates positively with CVD. Age, hypertension, PIR affect CVD risk most in general. For AO patients, education, gender, ethnicity are key CVD risk factors. |
| format | Article |
| id | doaj-art-69fb5e67423648c4ac0852a600a63776 |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-69fb5e67423648c4ac0852a600a637762025-02-09T12:40:24ZengBMCBMC Medical Informatics and Decision Making1472-69472025-02-0125111810.1186/s12911-025-02885-0Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learningXiyu Cao0Jianli Ma1Xiaoyi He2Yufei Liu3Yang Yang4Yaqi Wang5Chuantao Zhang6Department of Respiratory Medicine, Hospital of Chengdu University of Traditional Chinese MedicineDepartment of Respiratory Medicine, Hospital of Chengdu University of Traditional Chinese MedicineColumbia UniversityDepartment of Respiratory Medicine, Hospital of Chengdu University of Traditional Chinese MedicineDepartment of Gastroenterology, Hospital of Chengdu University of Traditional Chinese MedicineDepartment of Respiratory Medicine, Hospital of Chengdu University of Traditional Chinese MedicineDepartment of Respiratory Medicine, Hospital of Chengdu University of Traditional Chinese MedicineAbstract Background Respiratory diseases and Cardiovascular Diseases (CVD) often coexist, with airflow obstruction (AO) severity closely linked to CVD incidence and mortality. As both conditions rise, early identification and intervention in risk populations are crucial. However, current CVD risk models inadequately consider AO as an independent risk factor. Therefore, developing an accurate risk prediction model can help identify and intervene early. Methods This study used the National Health and Nutrition Examination Survey (NHANES) III (1988–1994) and NHANES 2007–2012 datasets. Inclusion criteria were participants aged over 40 with complete AO and CVD data; exclusions were those with missing key data. Analysis included 12 variables: age, gender, race, PIR, education, smoking, alcohol, BMI, hyperlipidemia, hypertension, diabetes, and AO. Logistic regression analyzed the association between AO and CVD, with sensitivity and subgroup analyses. Six ML models predicted CVD risk for the general population, using AO as a predictor. RandomizedSearchCV with 5-fold cross-validation was used for hyperparameter optimization. Models were evaluated by AUC, accuracy, precision, recall, F1 score, and Brier score, with the SHapley Additive exPlanations (SHAP) enhancing explainability. A separate ML model was built for the subpopulation with AO, evaluated similarly. Results The cross-sectional analysis showed that there was a significant positive correlation between AO occurrence and CVD prevalence, indicating that AO is an important risk factor for CVD (all P < 0.05). For the general population, the XGBoost model was selected as the optimal model for predicting CVD risk (AUC = 0.7508, AP = 0.3186). The top three features in terms of importance were age, hypertension, and PIR. For the subpopulation with airflow obstruction, the XGBoost model was also selected as the optimal model for predicting CVD risk (AUC = 0.6645, AP = 0.3545). SHAP shows that education level has the greatest impact on predicting CVD risk, followed by gender and race. Conclusion AO correlates positively with CVD. Age, hypertension, PIR affect CVD risk most in general. For AO patients, education, gender, ethnicity are key CVD risk factors.https://doi.org/10.1186/s12911-025-02885-0Cardiovascular diseaseAirflow obstructionCo-morbidityMachine learningPrediction model |
| spellingShingle | Xiyu Cao Jianli Ma Xiaoyi He Yufei Liu Yang Yang Yaqi Wang Chuantao Zhang Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning BMC Medical Informatics and Decision Making Cardiovascular disease Airflow obstruction Co-morbidity Machine learning Prediction model |
| title | Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning |
| title_full | Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning |
| title_fullStr | Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning |
| title_full_unstemmed | Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning |
| title_short | Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning |
| title_sort | unlocking the link predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning |
| topic | Cardiovascular disease Airflow obstruction Co-morbidity Machine learning Prediction model |
| url | https://doi.org/10.1186/s12911-025-02885-0 |
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