A Research Review on Fetal Heart Disease Detection Techniques

The study is intended to be in accordance with recent World Health Organization (WHO) findings on Congenital heart disorders (CHD), the most frequent heart condition and feasible detection methods. Globally, 2.1 million people have been diagnosed with CHD in 2020, with 29 percent of them dying as a result of cardiac stress on a global scale each year. Southeast Asia and the Indian subcontinent are the most notable regions of Fetal heart disease (FHD) primarily affecting child aged 2 months and older, but it is still spreading in underdeveloped nations owing to a lack of health care. According to current WHO studies, the necessity of precise techniques for prediction at an early stage through effective monthly examination of the heart to detect cardiac disorders are crucial for health care planning. The volume and size of medical datasets are continually growing and enhanced data mining tools might assist clinicians in making efficient and appropriate judgments. The problems of FHD data include feature selection, sample quantity, sample imbalance and lack of magnitude for some characteristics such as maximum and minimum feret diameters. This study is primarily concerned with improving feature selection and reducing the number of risk features. Meta-heuristic techniques are proposed in this paper to choose salient aspects of the FHD.