Journal of Management Information and Decision Sciences (Print ISSN: 1524-7252; Online ISSN: 1532-5806)

Abstract

Prediction of congenital heart diseases in children using machine learning

Author(s): Fatma Saeed Al Ali, Sara Abdullah Ali Al Hammadi, Abdesselam Redouane, Muhammad Usman Tariq

 Congenital Heart Diseases (CHD) are conditions that are present at birth and can affect the structure of a baby’s heart and the way it works. They are the most common type of birth defect that is most commonly diagnosed in newborns, affecting approximately 0.8% to 1.2% of live births worldwide. The incidence and mortality of CHDs vary worldwide. The causes of CHDs are unknown. Some children have heart defects because of changes in their genes. CHDs are also thought to be caused by a combination of genes and other factors such as environmental factors, the diet of the mother, the mother’s health conditions, or the mother’s medication use during pregnancy.

The diagnosis of CHDs may occur either during pregnancy or after birth, or later in life, during childhood or adulthood. The signs and symptoms of CHDs depend on the type and severity of the particular type of CHD present in the individual. During pregnancy, CHDs may be diagnosed using a special type of ultrasound called a fetal echocardiogram, which creates ultrasound pictures of the developing baby's heart. Routine medical check-ups often lead to the detection of minor as well as major defects. If a healthcare provider suspects a CHD may be present, the child can get several tests such as an echocardiogram to confirm the diagnosis.

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