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Predicting congenital heart defects: A comparison of three data mining methods
Yanhong Luo1, Zhi Li1, Husheng Guo2
1Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi Province, People's Republic of China.
Insights
Machine learning models effectively predict congenital heart defects (CHD) risk in pregnant women. The Weighted Support Vector Machine (WSVM) model demonstrated superior performance in identifying high-risk pregnancies.
Area of Science:
- Epidemiology
- Biostatistics
- Machine Learning
Background:
- Congenital heart defects (CHD) are a significant public health concern, particularly in China.
- Existing risk factor studies for CHD often lack robust predictive validation, especially using population-based cross-sectional data.
- Predicting CHD risk pre- and during pregnancy is crucial for early intervention.
Purpose of the Study:
- To develop and validate machine learning models for predicting maternal risk of bearing children with CHD.
- To compare the predictive performance of different machine learning algorithms using population-based data.
- To identify key risk factors for CHD prediction.
Main Methods:
- A retrospective cross-sectional epidemiological survey of birth defects (2006-2008) in Shanxi Province, China, involving 33,831 live births and 78 CHD cases.
- Development of nine synthetic variables including maternal age, socioeconomic factors, medical history, and lifestyle.
- Training and validation of Weighted Support Vector Machine (WSVM), Weighted Random Forest (WRF), and logistic regression (Logit) models using two-thirds and one-third of the data, respectively.
- Performance evaluation using True Positive Rate (TPR), True Negative Rate (TNR), Accuracy (ACC), Area Under the Curve (AUC), G-means, and Weighted Accuracy (WTacc), with 1000 data partitioning repetitions.
Main Results:
- All three models achieved high predictive performance, with TPR > 0.65 and TNR > 0.93.
- The WSVM model exhibited the highest performance across key metrics including TPR, Weighted Accuracy (WTacc), AUC, and G-means.
- The models demonstrated significant precision in identifying high-risk groups for CHD.
Conclusions:
- Machine learning models, particularly WSVM, offer a precise and effective approach for predicting congenital heart defects risk.
- These models can aid in identifying high-risk pregnancies, enabling timely interventions.
- The developed methodology holds potential for application in predicting other birth defects and diseases.
Abstract:
Congenital heart defects (CHD) is one of the most common birth defects in China. Many studies have examined risk factors for CHD, but their predictive abilities have not been evaluated. In particular, few studies have attempted to predict risks of CHD from, necessarily unbalanced, population-based cross-sectional data. Therefore, we developed and validated machine learning models for predicting, before and during pregnancy, women's risks of bearing children with CHD. We compared the results of these models in a large-scale, comprehensive population-based retrospective cross-sectional epidemiological survey of birth defects in six counties in Shanxi Province, China, covering 2006 to 2008. This contained 78 cases of CHD among 33831 live births. We constructed nine synthetic variables to use in the models: maternal age, annual per capita income, family history, maternal history of illness, nutrition and folic acid deficiency, maternal illness in pregnancy, medication use in pregnancy, environmental risk factors in pregnancy, and unhealthy maternal lifestyle in pregnancy. The machine learning algorithms Weighted Support Vector Machine (WSVM) and Weighted Random Forest (WRF) were trained on, and a logistic regression (Logit) was fitted to, two-thirds of the data. Their predictive abilities were then tested in the remaining data. True positive rate (TPR), true negative rate (TNR), accuracy (ACC), area under the curves (AUC), G-means, and Weighted accuracy (WTacc) were used to compare the classification performance of the models. Median values, from repeating the data partitioning 1000 times, were used in all comparisons. The TPR and TNR of the three classifiers were above 0.65 and 0.93, respectively, better than any reported in the literature. TPR, wtACC, AUC and G were highest for WSVM, showing that it performed best. All three models are precise enough to identify groups at high risk of CHD. They should all be considered for future investigations of other birth defects and diseases.
