Non-Invasive Parameter-Based Machine Learning Models for Accurate Diagnosis of Congenital Heart Disease

Abstract

Recent studies have focused on early detection of congenital heart diseases (CHD), as the number of cases continues to rise, making it a prevalent condition worldwide. CHD commonly affects newborn babies. Non-Invasive Parameter-Based Machine Learning Models for Accurate Diagnosis of Congenital Heart Disease study has explored the use of non-clinical data to detect CHD, enabling easy identification without affecting the fetus. Machine and deep learning have been employed to detect CHD using this dataset. While previous research has explored various models, the adoption of an artificial neural network (ANN) model has notably enhanced the performance of CHD detection. The findings of the study indicate that these models achieved high accuracy, reaching 99.79%. Hence, it is advised to develop a model capable of achieving higher accuracy within a shorter timeframe for the early detection of CHD in unborn babies using non-invasive datasets.

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