Paper Info
Title
Early Prediction of LBW Cases via Minimum Error Rate Classifier: A Statistical Machine Learning Approach
Abstract
Low Birth weight (LBW) acts as an indicator of sickness in newborn babies. LBW is closely associated with infant mortality as well as various health outcomes later in life. Various studies show strong correlation between maternal health during pregnancy and the child's birth weight. This manuscript exploits machine learning techniques to gain useful information from health indicators of pregnant women for early detection of potential LBW cases. The forecasting problem has been reformulated as a classification problem between LBW and NOT-LBW classes using the Bayes' minimum error rate classifier rendering LBW detection as a binary machine classification problem. Expectedly, the proposed model achieved accuracy of 96.77%. Indian health care data was used to construct decision rules to be extrapolated to predictive health care in smart cities. A screening tool based on the decision model is developed to assist health care professionals in Obstetrics and Gynecology (OBG). Index Terms-Low Birth weight (LBW), Smart health informatics, Minimum error rate classifier, Predictive analytics, Machine Learning (ML), Feature Ranking.
Year
DOI
Venue
2017
10.1109/SMARTCOMP.2017.7947002
2017 IEEE International Conference on Smart Computing (SMARTCOMP)
Keywords
Field
DocType
Low Birth weight (LBW),Smart health informatics,Minimum error rate classifier,Predictive analytics,Machine Learning (ML),Feature Ranking
Health care,Decision rule,Health indicator,Computer science,Predictive analytics,Word error rate,Birth weight,Decision model,Artificial intelligence,Health informatics,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-5090-6518-9
1
0.37
References 
Authors
0
3
Name
Order
Citations
PageRank
Anisha R. Yarlapati110.37
Sudeepa Roy Dey210.71
Snehanshu Saha34617.96