Early Prediction of LBW Cases via Minimum Error Rate Classifier: A Statistical Machine Learning Approach

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).