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What is Baysean Theorem

Chapter 9
Innovations in Global Maternal Health: Improving Prenatal and Postnatal Care Practices
CRISP-DM methodology
Published in Chapter:
A Predictive Analytic Model for Maternal Morbidity
Edgardo Palza (École de technologie supérieure, Canada), Jorge Sanchez (Universidad Peruana Unión, Peru), Guillermo Mamani (Universidad Peruana Unión, Peru), Percy Pacora (Hospital Nacional Docente Madre Niño “San Bartolomé”, Peru), Alain Abran (École de Technologie Supérieure, Canada), and Jane Moon (University of Melbourne, Australia)
Copyright: © 2020 |Pages: 23
DOI: 10.4018/978-1-7998-2351-3.ch009
Abstract
This chapter presents a predictive analytic model for preventing neonatal morbidity through the analysis of patterns of risky behavior regarding morbidity in newborns. The chapter presents the design and implementation of a forecasting model of Neonatal morbidity. The model developed is based on artificial intelligence using Bayesian Networks, Influence Diagrams and principles of traditional statistics. The model research is based on a repository of 10,000 medical records at a hospital in Peru. The model aims to identify the factors that are causes of morbidity in newborns, is based on data mining techniques and developed using the CRISP-DM methodology.
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