Abstract
Postpartum haemorrhage (PPH) is the loss of blood above 500 ml during vaginal or caesarean deliveries. It is difficult to find a PPH in an earlier stage, so pregnant women are exposed to excess blood loss that makes them suffer and die. Antenatal practices help in identifying risk factors, and modern technology is used to overcome the risk. Still, the morbidity rate and the mortality arise due to the unpredicted and unexpected cause. PPH is still a significant cause of maternal morbidity and mortality worldwide. The novelty of this research work is to alert medical practitioner about excessive bleeding of pregnant women during childbirth. We are proposing an automation system using wearable devices to prevent pregnant women from the PPH. These devices measure parameters like temperature, pulse rate, blood pressure, and sweat rate of pregnant women. Fuzzy neural technique-based rules are used for each parameter to predict the risk in developing PPH and to evaluate the performance of proposed system for reducing mortality and morbidity rates. Our findings of experiment are carried on metrics of HPPH (high-level postpartum haemorrhage), NPPH (normal-level postpartum haemorrhage), and MPPH (medium-level postpartum haemorrhage) for 15 patients. Fuzzy output value of 1 indicates patient state with NPPH, 0 indicates patient state with HPPH, and values in between 0 and 1 indicate MPPH. Based on the sensitivity of the predicted values, medical attention is taken from doctors or nurses in nearby locations using Internet of Things infrastructure.