⚡ Quick Summary
This study explores the relationship between airflow obstruction (AO) and cardiovascular disease (CVD), utilizing machine learning to develop a predictive model. The findings indicate that AO is a significant risk factor for CVD, with the XGBoost model achieving an AUC of 0.7508 for the general population.
🔍 Key Details
- 📊 Dataset: NHANES III (1988-1994) and NHANES 2007-2012
- 🧩 Features used: 12 variables including age, gender, race, and AO
- ⚙️ Technology: Machine learning models, primarily XGBoost
- 🏆 Performance: General population AUC 0.7508, AO subpopulation AUC 0.6645
🔑 Key Takeaways
- 📊 AO is a critical risk factor for CVD, with a significant positive correlation.
- 💡 Age, hypertension, and PIR are the most influential factors for the general population.
- 👩🔬 Education level significantly impacts CVD risk in patients with AO.
- 🏆 XGBoost was the optimal model for predicting CVD risk in both populations.
- 🌍 Study highlights the need for improved CVD risk models that include AO.
- 📈 Machine learning enhances the accuracy of risk predictions.
- 🔍 SHAP analysis provides insights into feature importance for CVD risk.
📚 Background
The coexistence of respiratory diseases and cardiovascular diseases (CVD) poses a significant public health challenge. Airflow obstruction (AO) has been identified as a crucial factor influencing the incidence and mortality of CVD. However, traditional risk models often overlook AO as an independent risk factor, necessitating the development of more comprehensive predictive models.
🗒️ Study
This research utilized data from the National Health and Nutrition Examination Survey (NHANES), focusing on participants aged over 40 with complete AO and CVD data. The study analyzed 12 variables to assess the relationship between AO and CVD, employing logistic regression and various machine learning models to predict CVD risk.
📈 Results
The analysis revealed a significant positive correlation between AO occurrence and CVD prevalence (all P < 0.05). The XGBoost model emerged as the best performer for predicting CVD risk in the general population, achieving an AUC of 0.7508. In the AO subpopulation, the same model yielded an AUC of 0.6645, with education level being the most impactful feature.
🌍 Impact and Implications
The findings underscore the importance of incorporating AO into CVD risk assessments. By leveraging machine learning techniques, healthcare providers can enhance early identification and intervention strategies for at-risk populations. This research paves the way for more accurate and personalized healthcare approaches, ultimately improving patient outcomes in cardiovascular health.
🔮 Conclusion
This study highlights the significant role of airflow obstruction in predicting cardiovascular disease risk. The application of machine learning models, particularly XGBoost, demonstrates the potential for improved risk stratification in clinical settings. Continued research in this area is essential for developing effective interventions and enhancing patient care.
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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 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.
Author: [‘Cao X’, ‘Ma J’, ‘He X’, ‘Liu Y’, ‘Yang Y’, ‘Wang Y’, ‘Zhang C’]
Journal: BMC Med Inform Decis Mak
Citation: Cao X, et al. Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning. Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning. 2025; 25:50. doi: 10.1186/s12911-025-02885-0