Impact Analysis of Machine Learning Strategies on C-Section Risk Prediction

Authors

  • Lambodar Jena, Soumen Nayak, Sushruta Mishra

Abstract

Increase in caesarean section (C-Section) rates is a vital issue that needs attention at the global and local level. The major concern of public healthcare is toaddress these issues in terms of quality, equity and accessibility. Effective measures have been proposed and implemented in this work to reduce C-Section rates. The data from cesarean section dataset are classified and analyzed using different machine learning algorithms to predict class accurately in each case. It is very essential and vital to identify which machine learning algorithm classifies accurately the C-section and performs better for risk prediction. Three critical classifiers are considered to implement on the dataset and to measure their efficiency based on the value of various parameters.
Keywords: C-section,Classification, Machine Learning, Accuracy

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Published

2020-05-18

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Section

Articles