Published Paper Details:

People all over the world are now paying attention to the deaths of mothers and children. In low- and middle-income countries, maternal mortality is high, especially among teens and young adults. Healthcare professionals can use CTGs to keep an eye on the mother's heartbeat during pregnancy to make sure the baby is still alive and avoid these deaths. This study utilised machine learning techniques to conduct a risk factor analysis aimed at decreasing child and maternal mortality. This study assessed seven machine learning algorithms. Accuracy, precision, and recall were used to compare how well different categorisation algorithms worked. The random forest is the most accurate of the other algorithms, with an accuracy rate of 99.98%. At first, the dataset was not balanced. After using under sampling and oversampling methods, all of the algorithms worked very well. A primary objective of the current study was to forecast the risk factors associated with child and maternal mortality utilising clinical data. Ultrasound devices work by sending out a pulse and reading the response. This analysis is a good and cost-effective choice for healthcare professionals who want to keep mothers and children from dying.