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Child Mortality Prediction Using Machine Learning Techniques
Abstract
This project explores the application of machine learning techniques to predict child mortality by analyzing a dataset of 2,126 cardiotocogram (CTG) measurements, which detail fetal heart rate and uterine contraction patterns and have been meticulously classified by expert obstetricians. The study employs and compares various machine learning models, including logistic regression, decision trees, and neural networks, to assess their efficacy in identifying high-risk pregnancies. By leveraging these advanced predictive models, the research aims to enhance the accuracy of risk assessments and uncover critical patterns in fetal health data that may not be detectable through conventional methods. The ultimate goal is to support early interventions and improve child survival rates through data-driven decision-making in healthcare. This project has significant implications for enhancing predictive modeling and risk assessment in obstetrics, contributing to more effective and personalized maternal and child health care strategies.
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