MOTIVATION Reduction in Maternal Mortality is one of the Sustainable Development Goals (SDG) of the United Nations (UN) [cite here]. It has been estimated that 529,000 women die yearly from pregnancy-related causes annually [Nour]; and 99% of these deaths occur in developing nations [Nour]. A few of the causes of such high maternal mortality include sepsis, obstructed labour, eclampsia, postpartum haemorrhage etc. Access to adequate and quality healthcare is limited in developing nations and when these are available, their costs are usually out of reach of the poor and lower-middle class. Hence, it is imperative to build a solution that can help mitigate the high numbers of preventable deaths especially maternal mortality in developing nations. work more on this
METHODOLOGY 1. Get the data containing various variables that affect maternal mortality 2. Create a machine learning model that learns this data. 3. Create a flask application that has a form, taking the causes of maternal mortality (in the data) as inputs. 4. Pass these inputs to the model in order to predict the risk level of the pregnant woman.
PRACTICAL APPLICATIONS 1. Can be used as a diagnosis tool to detect the risk level of pregnant mothers 2. This can help reduce the wait times present in maternal care in developing countries.
Live Site \n You can view the live site here: https://materna-ab31b6c885be.herokuapp.com/
References Nour, Nawal M. "An introduction to maternal mortality." Reviews in obstetrics and gynecology 1.2 (2008): 77.