A Method for Predicting and Classifying Fetus Health Using Machine Learning
Keywords:
Gini index, Machine Learning, Prediction analysis, Random Forest ClassificationAbstract
Each year on average 3 million pregnant women and newborns die every 15 seconds mostly from preventable causes, according to the estimates released by UNICEF, WHO, the UN population division, and the World Bank group. Birth is a joyful occasion everywhere. In the United States, complications during pregnancy or delivery result in the deaths of approximately 700 women annually. Due to covid, traffic in metropolitan areas, and long office hours, it is currently extremely difficult to leave the house even for a medical checkup. Next, imagine that a pregnant woman needs to go to the doctor for her regular checkup. As part of her examination, she will travel to hospitals, laboratories, and other locations. As a result, she will have to spend a lot of money and work a lot, which will make her exhausted, which is bad for both her and her unborn child. A fetus is a child that is still in the embryonic stage when it is born. During this time, the fetus grows and develops, requiring regular examinations. We are all aware that a pregnancy lasts for nine months, during which time a variety of factors can cause the newborn to be disabled or die, which is a very serious situation that must be avoided. One of the most important tools for analyzing the health of the fetus in the womb is performing a CTG (continuous cardiotocography), which is commonly used to evaluate the heartbeat and the health of the fetus during pregnancy.
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