The smart earlier prediction of conginental heart disease in pregnancy using deep learning model

Deep learning models have been used in the early prediction of congenital heart defects in pregnant women. These models exploit data from a variety of sources to create predictive models that can accurately identify the presence of certain types of heart defects before they become symptomatic. These sources include maternal demographic and medical information, fetal echocardiography imaging, and contemporary deep learning techniques. By combining these sources, deep learning models have been able to accurately predict the presence of congenital heart defects with greater accuracy than traditional approaches. This early prediction can enable healthcare professionals to initiate more timely treatments, improve outcomes, and ultimately provide better care to pregnant women. In this paper, a smart earlier prediction of conginental heart disease in pregnancy has proposed using deep learning model. The proposed model achieved 92.6% accuracy, 90% precision, 90.1% recall and 94.7% F1-score