Please use this identifier to cite or link to this item:
Title: Data-Driven Stillbirth Prediction and Analysis of Risk Factors in Pregnancy
Authors: Unnikrishnan A.
Chandrasekaran K.
Shukla A.
Issue Date: 2021
Citation: Advances in Intelligent Systems and Computing , Vol. 1245 , , p. 511 - 523
Abstract: One of the main issues in developing countries is the lack of policies for ensuring good public health conditions in rural areas. Maternal and child health care is one such area that has not improved in developing countries. Although child health has improved noticeably over the years, infant or under-5-mortality has not become any better. There remain major knowledge gaps in our understanding of how factors such as prenatal care, antenatal care, social and economic backgrounds, living conditions and lifestyle of pregnant women and their family members affect the pregnancy outcomes. Understanding such factors that affect the poor pregnancy outcome helps in formulating plans to prevent such issues and to treat them effectively. Determining health policies will be easier from a deeper analysis of such factors involved. This paper discusses some of the key machine learning techniques to predict the pregnancy outcome as a stillbirth or not and analyze some of the factors that majorly cause stillbirth. © 2021, Springer Nature Singapore Pte Ltd.
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.