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Using machine learning algorithms to identify chronic heart disease: National Health and Nutrition Examination Survey
Xiaofei Chen1, Dingjie Guo1, Yashan Wang1
1Epidemiology and Statistics, School of Public Health, Jilin University.
Insights
Machine learning models effectively predict chronic heart disease (CHD) risk using population data. Key predictors include aspirin use, chest pain, and supplement intake, aiding early diagnosis.
Area of Science:
- Cardiology
- Medical Informatics
- Public Health
Background:
- Increasing prevalence of heart disease necessitates improved risk assessment strategies.
- Early diagnosis and treatment of high-risk populations are crucial for managing chronic heart disease (CHD).
Purpose of the Study:
- To establish a risk assessment model for CHD using machine learning.
- To identify key risk factors for early CHD diagnosis and intervention.
Main Methods:
- Utilized four machine learning models: logistic regression, support vector machines (SVM), random forests, and extreme gradient boosting (XGBoost).
- Analyzed data from 14,971 participants in the National Health and Nutrition Examination Survey (2011-2018).
- Evaluated model performance using the area under the receiver-operator curve (AUC).
Main Results:
- Support vector machines (SVM) demonstrated the highest classification performance with an AUC of 0.898.
- Logistic regression (0.895) and random forests (0.894) also showed strong performance; XGBoost yielded the lowest AUC (0.891).
- No significant performance differences were observed among the four algorithms. Key predictors identified were low-dose aspirin use, chest pain/discomfort, and dietary supplement intake.
Conclusions:
- All four machine learning classifiers accurately predicted CHD occurrence from population survey data.
- Variable importance analysis identified significant predictors, offering potential for clinical application.
- These models can aid in the early identification and management of individuals at risk for chronic heart disease.
Objective:
The number of heart disease patients is increasing. Establishing a risk assessment model for chronic heart disease (CHD) based on risk factors is beneficial for early diagnosis and timely treatment of high-risk populations.
Methods:
Four machine learning models, including logistic regression, support vector machines (SVM), random forests, and extreme gradient boosting (XGBoost), were used to evaluate the CHD among 14 971 participants in the National Health and Nutrition Examination Survey from 2011 to 2018. The area under the receiver-operator curve (AUC) is the indicator that we evaluate the model.
Results:
In four kinds of models, SVM has the best classification performance (AUC = 0.898), and the AUC value of logistic regression and random forest were 0.895 and 0.894, respectively. Although XGBoost performed the worst with an AUC value of 0.891. There was no significant difference among the four algorithms. In the importance analysis of variables, the three most important variables were taking low-dose aspirin, chest pain or discomfort, and total amount of dietary supplements taken.
Conclusion:
All four machine learning classifiers can identify the occurrence of CHD based on population survey data. We also determined the contribution of variables in the prediction, which can further explore their effectiveness in actual clinical data.
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