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

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.

[1]  Dauda Eneyamire Suleiman,et al.  Reviving hospital autopsy in Nigeria: An urgent call for action , 2015 .

[2]  Jong Yeol Kim,et al.  Indicators of hypertriglyceridemia from anthropometric measures based on data mining , 2015, Comput. Biol. Medicine.

[3]  Alexander Y. Shestopaloff,et al.  Naive Bayes classifiers for verbal autopsies: comparison to physician-based classification for 21,000 child and adult deaths , 2015, BMC Medicine.

[4]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[5]  Philip S. Yu,et al.  Determinants of HIV-induced brain changes in three different periods of the early clinical course: A data mining analysis , 2015, NeuroImage: Clinical.

[6]  Danilo López,et al.  Algorithm and Software Based on Multilayer Perceptron Neural Networks for Estimating Channel Use in the Spectral Decision Stage in Cognitive Radio Networks , 2016 .

[7]  Juan Lorenzo Ginori,et al.  Algoritmos de aprendizaje automático para la clasificación de neuronas piramidales afectadas por el envejecimiento , 2016 .

[8]  Mr. A. A. Dange,et al.  Survey on Assess Co-Morbid Risk of Diabetes Mellitus by using Split and Merge Association Rule Summarization Techniques , 2016 .

[9]  Liyana Shuib,et al.  Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection , 2017, PloS one.

[10]  George T. S. Ho,et al.  An intelligent medical Replenishment System for managing the medical resources in the healthcare industry , 2016, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[11]  Shuib Liyana,et al.  Automatic Text Classification of ICD-10 Related CoD from Complex and Free Text Forensic Autopsy Reports , 2016 .

[12]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[13]  Lisbeth Rodríguez-Mazahua,et al.  Association Analysis of Medical Opinions About the Non-realization of Autopsies in a Mexican Hospital , 2018 .

[14]  Peter A. Flach,et al.  Confirmation-Guided Discovery of First-Order Rules with Tertius , 2004, Machine Learning.

[15]  Taeseon Yoon,et al.  Comparison of episodes of mosquito borne disease: Dengue, yellow fever, west nile, and filariasis with decision tree, apriori algorithm , 2016, 2016 18th International Conference on Advanced Communication Technology (ICACT).

[16]  Vijetha Vemulapalli,et al.  Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data , 2016, Artif. Intell. Medicine.

[17]  Tobias Scheffer.  Finding association rules that trade support optimally against confidence , 2005 .

[18]  Ana Isabel Oviedo Carrascal,et al.  Minería de datos: Aportes y tendencias en el servicio de salud de ciudades inteligentes , 2015 .

[19]  Young-Bae Park,et al.  Association Rule Mining in Korean Herbal Prescriptions of the Early 20th Century , 2015 .

[20]  Tesis De Grado,et al.  OPTIMIZACIÓN DE REDES BAYESIANAS BASADO EN TÉCNICAS DE APRENDIZAJE POR INDUCCIÓN , 2005 .

[21]  N. J. R. Muniraj,et al.  Survey on medical diagnosis using data mining techniques , 2013, 2013 International Conference on Optical Imaging Sensor and Security (ICOSS).

[22]  Pedro L. Alfonzo,et al.  SIMULACIÓN DEL RAZONAMIENTO EN EL PROCESO DE IDENTIFICACIÓN BOTÁNICA BASADO EN REDES BAYESIANAS , 2017 .

[23]  Kamel Hamrouni,et al.  Ontology Knowledge Mining Based Association Rules Ranking , 2016, KES.

[24]  Asdrúbal López Chau,et al.  Preliminary Results of an Analysis Using Association Rules to Find Relations between Medical Opinions About the non-Realization of Autopsies in a Mexican Hospital , 2017, Res. Comput. Sci..

[25]  May D. Wang,et al.  icuARM-II: improving the reliability of personalized risk prediction in pediatric intensive care units , 2014, BCB.

[26]  Luca Cagliero,et al.  MeTA , 2015, ACM Trans. Intell. Syst. Technol..

[27]  Yoichi Hayashi,et al.  Rule extraction using Recursive-Rule extraction algorithm with J48graft combined with sampling selection techniques for the diagnosis of type 2 diabetes mellitus in the Pima Indian dataset , 2016 .

[28]  Sabine Rohrmann,et al.  Changes in autopsy rates among cancer patients and their impact on cancer statistics from a public health point of view: a longitudinal study from 1980 to 2010 with data from Cancer Registry Zurich , 2015, Virchows Archiv.

[29]  Anazida Zainal,et al.  Enhanced affixation word stemmer with stemming error reducer to solve affixation stemming errors , 2016 .

[30]  Taeseon Yoon,et al.  Analysis of anti-cancer cytokines by Apriori algorithm, decision tree, and SVM , 2015, 2015 International Conference on Big Data and Smart Computing (BIGCOMP).

[31]  Alper Kursat Uysal,et al.  An improved global feature selection scheme for text classification , 2016, Expert Syst. Appl..

[32]  Jirapond Muangprathub,et al.  A Web-Based Medical Diagnostic System using Data Mining Technique , 2016 .

[33]  Marta Mayorga,et al.  [Autopsy in clinical oncology: is it in crisis?]. , 2011, Medicina clinica.

[34]  Tina R. Patil,et al.  Performance Analysis of Naive Bayes and J 48 Classification Algorithm for Data Classification , 2013 .

[35]  Hamidah Ibrahim,et al.  Intelligent cooperative web caching policies for media objects based on decision tree supervised machine learning algorithm , 2014 .

[36]  Elizabeth León-Guzmán,et al.  An approach to the risk analysis of diabetes mellitus type 2 in a health care provider entity of Colombia using business intelligence , 2012, 2012 6th Euro American Conference on Telematics and Information Systems (EATIS).

[37]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[38]  Aytug Onan,et al.  Ensemble of keyword extraction methods and classifiers in text classification , 2016, Expert Syst. Appl..