A Comprehensive Study to Analyze Congenital Heart Disease Detection

Congenital heart disease (CHD) is one of the most common causes of death in the world. It is distinguished by a high death rate brought on by the fetus's congenital defects. Both the incidence of congestive heart failure and the prevalence of CHD have grown recently. This article focuses on the use of non-clinical data from pregnant women, child, adults and machine learning to predict congenital heart disease. The study demonstrates how well the Artificial Neural Network (ANN) model performs in detecting CHD, outperforming conventional methods. The exceptional performance of the ANN model indicates that it can handle complex datasets, which suggests that it could be useful in more predictive healthcare applications. The results encourage further investigation into machine learning frameworks that employ noninvasive data to predict various health problems, potentially revolutionizing the medical care. This article focuses on the application of dermatoglyphics in predicting CHD risk factors and highlighting the importance of incorporating it into preventive cardiology in order to possibly support early intervention techniques.