Measurement of Non-Contact Uterine Fundus in Second and Third Trimester of Pregnancy Using Machine Learning

Abstract

Regular pregnancy checks provide good data points for pregnant women. The information obtained is expected to have an impact on the health of the baby from the womb until the baby is born. Fetal monitoring and examination have developed significantly in terms of the use of technology as a tool for control and examination. In harmony with digitalization being promoted by the government through digital-based health services. On the other hand, apart from causing things to change the order of life, including behavior and habits, one of the things is the role of technology, which is increasingly vital in its initial nature as a tool for human life, including in the health sector. One of the things that can be integrated with technology is pregnancy monitoring for pregnant women, especially measuring the symphysis-fundal height. Professionals should have an accurate prediction method for making diagnoses during prenatal care, so they can detect them as early as possible. This can be assisted by detecting the size of the symphysis-fundal height using digital images. This study took data from 98 respondents with a distribution of 4 data collection points. Of the 98 data, there were 10 invalid data because manual measurements failed to take place. This study took data from 98 respondents with a distribution of 4 data collection points. This study is the best accuracy at the Banjaran midwife location, with a success rate of 93%. Further research is needed with a larger sample of pregnant women so that machine learning has an adequate trial to become a data-based maternal health. The implication of these findings is that machine learning has not been able to predict gestational age with precision, so it must be given more samples so that machine learning can continue to be trained in predicting and calculating after the end of the second and third trimesters.