Development of machine learning-based models to predict congenital heart disease - A matched case-control study

Q: What limitation in current CHD prediction tools motivated this study?

Current CHD prediction tools lack adequate interpretability and convenience, which hinders personalized CHD management planning.

Q: How were participants and data used to build the machine learning models?

Data from 1,759 participants in a matched case-control study across six surveillance hospitals were split into training and testing datasets using a 7:3 ratio.

Q: Which model performed best and what were the top predictors?

The XGB model showed the best performance. The top 3 predictors were living in rural areas, low wealth index, and folic acid supplements for less than 90 days.

Aug 4, 2026

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Congenital heart disease (CHD) prediction tools remain limited by insufficient interpretability and usability, restricting progress toward personalized CHD management. A machine learning–based risk stratification model was developed to improve convenience and explainability. Using 1,759 participants from a CHD matched case-control study across six birth defects surveillance hospitals in Xi’an, Shaanxi (Jan 2014–Dec 2016), data were split 7:3 into training and testing sets.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Methods

Data source and study design

Feature selection and model building

Model evaluation metrics

Results

Predictive performance

Key predictors

Risk score calibration and stratification

Conclusion

Clinical implications

International Journal of Medical Informatics 195 (2025) 105741 Contents lists available at ScienceDirect International Journal of Medical Informatics journal [homepage: www.elsevier.com/locate/ijmedinf](homepage: www.elsevier.com/locate/ijmedinf) | Development of machine learning-based models to predict congenital heart disease: A matched case-control study
Shutong Zhang a, Chenxi Kang a, Jing Cuia, Haodan Xuea, Shanshan Zhao a, Yukui Chena, Haixia Lua, Lu Ye b, Duolao Wang c,d, Fangyao Chena, Yaling Zhao a, Leilei Peia,*, Pengfei Qu e,f,**
a Department of Epidemiology and Health Statistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi 710061, China b Shaanxi Eye Hospital, Xi’an People’s Hospital (Xi’an Fourth Hospital), Xi’an, China
c Biostatistics Unit, Department of Clinical Sciences, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
d Department of Neurology, Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
e Translational Medicine Center, Northwest Women’s and Children’s Hospital, Xi’an 710061, China
f Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Chaoyang, Beijing 100026, China | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
Congenital heart disease Prediction
Risk score
Machine learning Web tool | | 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. | 1. Introduction Congenital heart disease (CHD) significantly contributes to infant and child mortality and morbidity, and is the most common birth defect accounting for one-third of all congenital abnormalities worldwide.[1,2] According to the Global Burden of Disease (GBD) study, it was estimated that approximately 3 million newborns were born with congenital heart anomalies in 2019 worldwide. [3] In China, the prevalence of

FAQs

What limitation in current CHD prediction tools motivated this study?

Current CHD prediction tools lack adequate interpretability and convenience, which hinders personalized CHD management planning.

How were participants and data used to build the machine learning models?

Data from 1,759 participants in a matched case-control study across six surveillance hospitals were split into training and testing datasets using a 7:3 ratio.

Which model performed best and what were the top predictors?

The XGB model showed the best performance. The top 3 predictors were living in rural areas, low wealth index, and folic acid supplements for less than 90 days.

Aug 4, 2026

Upload by Liam