Key result
A Random Forest machine learning model analyzing maternal and paternal health factors predicted the risk of having a child with congenital heart defects with an accuracy of 97.37%.
Why the study?
Congenital heart defects are a leading cause of infant mortality and often remain undiagnosed in resource-limited settings like Egypt, making AI-driven early detection based on risk factors valuable.
Can machine learning models using maternal and paternal health factors accurately predict the risk of having a child with congenital heart defects?
Case-Control (n=571)
Can machine learning models using maternal and paternal health factors accurately predict the risk of having a child with congenital heart defects?
Answers are grounded in this paper and cite their sources.
Machine learning models, particularly Random Forest, demonstrate high accuracy in predicting the risk of congenital heart defects based on parental health factors, offering a potential tool for early detection.
Congenital heart defects (CHD) are heart malformations present at birth, affecting heart function and circulation, and are a leading cause of infant mortality. CHD can result from genetic, environmental, and maternal health factors, making early detection essential. Early diagnosis allows for timely intervention, reducing risks like heart failure or stroke. In countries like Egypt, CHD often remains undiagnosed due to limited healthcare resources. Artificial intelligence (AI) can improve early detection by analyzing risk factors. This study presents a predictive model for CHD using maternal and paternal health factors. Data was collected from 571 families: 260 with a CHD-affected child and 311 with healthy children. After preprocessing the data, ten machine learning models were tested, including Random Forest (RF), Decision Tree (DT), and MLP Classifier. RF achieved the highest accuracy at 97.37%, followed by DT at 96.49%, and MLP at 92.96%. The results show AI's potential in predicting CHD, supporting early diagnosis and improving infant outcomes.
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Abdo et al. (2025) conducted a case-control in Congenital heart defects (n=571). Machine learning models (Random Forest, Decision Tree, MLP Classifier) was evaluated on Model accuracy. A Random Forest machine learning model analyzing maternal and paternal health factors predicted the risk of having a child with congenital heart defects with an accuracy of 97.37%.
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