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Scientific Programming
Volume 2018, Article ID 4304017, 21 pages
https://doi.org/10.1155/2018/4304017
Research Article

Analysis of Medical Opinions about the Nonrealization of Autopsies in a Mexican Hospital Using Association Rules and Bayesian Networks

1División de Estudios de Posgrado e Investigación, Instituto Tecnológico de Orizaba, Orizaba, VER, Mexico
2Hospital Regional de Río Blanco (HRRB), Río Blanco, VER, Mexico
3Universidad Autónoma del Estado de México, Centro Universitario UAEM Texcoco, Texcoco, MEX, Mexico
4CONACYT-Instituto Tecnológico de Orizaba, Orizaba, VER, Mexico
5Universidad Autónoma del Estado de México, Centro Universitario UAEM Zumpango, Zumpango, MEX, Mexico

Correspondence should be addressed to Lisbeth RodrĂ­guez-Mazahua; moc.liamg@80rhtebsil

Received 6 May 2017; Revised 28 September 2017; Accepted 9 January 2018; Published 13 February 2018

Academic Editor: José María Álvarez-Rodríguez

Copyright © 2018 Elayne Rubio Delgado et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This research identifies the factors influencing the reduction of autopsies in a hospital of Veracruz. The study is based on the application of data mining techniques such as association rules and Bayesian networks in data sets obtained from opinions of physicians. We analyzed, for the exploration and extraction of the knowledge, algorithms like Apriori, FPGrowth, PredictiveApriori, Tertius, J48, NaiveBayes, MultilayerPerceptron, and BayesNet, all of them provided by the API of WEKA. To generate mining models and present the new knowledge in natural language, we also developed a web application. The results presented in this study are those obtained from the best-evaluated algorithms, which have been validated by specialists in the field of pathology.