Predicting Low Birth Weight (LBW) Cases in Early Pregnancy using Machine Learning Approaches Top of Form
Abstract
LBW, indicating newborn health issues, is linked to infant mortality and long-term health concerns. This study utilizes machine learning to detect potential LBW cases early by analyzing maternal health indicators. It frames the problem as a binary classification task and achieves improved accuracy. Decision rules derived from Indian healthcare data aid in predictive healthcare for smart cities, with a screening tool developed for Obstetrics and Gynecology professionals.
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Introduction
The WHO Maternal Health Program 1992 highlights the rising concern of Low Birth Weight (LBW), expected to increase by 12% annually. LBW contributes to neonatal deaths globally, with significant health risks for affected children. LBW, defined as babies under 2500g, poses challenges worldwide, particularly in developing countries like India. Maternal health strongly influences birth weight, emphasizing the importance of early detection and intervention to mitigate LBW risks.
Conclusion
We've built an ML model using Flask in Python to predict Low Birth Weight. Among XGBoost, Random Forest, Decision Tree, and Support Vector Classifiers, Decision Tree Classifier achieved the best accuracy.