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Development of machine learning-based models to predict congenital heart disease: A matched case-control study
Shutong Zhang1, Chenxi Kang1, Jing Cui1
1Department of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, China.
Insights
A new machine learning model accurately predicts congenital heart disease (CHD) risk using factors like rural living and folic acid use. This tool aids in identifying high-risk pregnancies for better CHD management.
Area of Science:
- Medical Informatics
- Public Health
- Genetics
Background:
- Current congenital heart disease (CHD) prediction tools lack interpretability and convenience.
- Personalized CHD management strategies are hindered by inadequate prediction tools.
Purpose of the Study:
- Develop and validate a machine learning-based risk stratification model for CHD prediction.
- Improve the accuracy and convenience of CHD risk assessment.
Main Methods:
- Utilized data from 1,759 participants in a case-control study (2014-2016) in Northwest China.
- Employed Least Absolute Shrinkage and Selection Operator (LASSO) for predictor selection from 47 variables.
- Built and evaluated five machine learning algorithms, including eXtreme Gradient Boosting (XGB), using metrics like AUROC, F1 score, and Brier score.
Main Results:
- The XGB model achieved superior performance with an AUROC of 0.772 in the testing dataset and 0.738 in external validation.
- Key predictors identified were rural living, low wealth index, and short-term folic acid supplementation (<90 days).
- The developed risk score effectively stratified participants into low, moderate, and high-risk categories, showing significant risk variations.
Conclusions:
- Machine learning offers a feasible and effective approach for CHD prediction.
- The risk scores can identify pregnant women at high risk for fetal CHD.
- This tool provides valuable insights for primary prevention and CHD management strategies.
Background:
The current congenital heart disease (CHD) prediction tools lack adequate interpretability and convenience, hindering the development of personalized CHD management strategies. We developed a machine learning-based risk stratification model for CHD prediction.
Methods:
This study utilized data from 1,759 participants in a case-control study of CHD conducted across six birth defects surveillance hospitals located in Xi'an, Shaanxi Province, Northwest China, spanning from January 2014 to December 2016. The data was partitioned into training and testing datasets with a ratio of 7:3. Predictors were selected from a total of 47 input variables through the Least Absolute Shrinkage and Selection Operator (LASSO). Five machine learning algorithms were used to build the CHD risk prediction models. Model performance was assessed based on a range of learning metrics, including the area under the receiver operating characteristic curve (AUROC), F1 score, and Brier score. Permutation feature importance was employed to elucidate the prediction model. The best-performing model was used to conduct the risk scores.
Results:
The eXtreme Gradient Boosting (XGB) model demonstrated superior performance among CHD prediction models, achieving an AUROC of 0.772 (95 % CI 0.728, 0.817) in the testing dataset and 0.738 (0.699, 0.775) in the external validation dataset. The pivotal predictors (top 3) identified by the model included living in rural areas, the low wealth index, and folic acid supplements (<90 days). The resultant risk score exhibited robust calibration capabilities. Utilizing the risk scores, participants were stratified into low, moderate, and high-risk categories, signifying substantial variations in CHD risk.
Conclusion:
This study underscores the feasibility and efficacy of employing a machine learning-based approach for CHD prediction. The risk scores exhibited potential in identifying pregnant women at high risk for fetal CHD, offering valuable insights for guiding primary prevention and CHD management.
