An eXtreme Gradient Boosting (XGB) machine learning model predicted congenital heart disease with an AUROC of 0.772 (95% CI 0.728-0.817) in the testing dataset.
Case-Control (n=1,759)
Yes
Does a machine learning-based model accurately predict the risk of fetal congenital heart disease?
A machine learning model utilizing maternal factors such as rural residence, wealth index, and folic acid supplementation can effectively predict the risk of fetal congenital heart disease.
Effect estimate: AUROC 0.772 (95% CI 0.728-0.817)
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.
Zhang et al. (Mon,) conducted a case-control in Congenital heart disease (CHD) (n=1,759). Machine learning-based risk stratification model (XGB) was evaluated on Prediction of congenital heart disease (AUROC) (AUROC 0.772, 95% CI 0.728-0.817). An eXtreme Gradient Boosting (XGB) machine learning model predicted congenital heart disease with an AUROC of 0.772 (95% CI 0.728-0.817) in the testing dataset.