Prediction of severe brain damage outcome using two data mining methods

Autor:	Jerzy W. Grzymala-Busse, Zdzislaw S. Hippe, Teresa Mroczek, Adam Bucinski, null Agnieszka, null Strepikowska, Andrzej Tutaj
Rok vydání:	2008
Předmět:	Computer science business.industry Rule induction Bayesian probability Bayesian network Machine learning computer.software_genre Outcome (probability) Expert system Set (abstract data type) Statistical classification Set theory Artificial intelligence Data mining business computer
Zdroj:	2008 Conference on Human System Interactions.
Popis:	In this paper we report our results on prediction of the Glasgow Outcome Scale for patients affected by severe brain damage. We used two data mining methods: the LEM2 rule induction system and the BeliefSEEKER system generating belief networks. Additionally, the original data set, with missing attribute values and numerical attributes, was mined by the MLEM2 system (a modified version of LEM2). Though our results show that the rule set induced by LEM2 is worse than the rule set obtained by conversion of a belief network generated by the BeliefSEEKER, it is possible to simplify the LEM2 rule set to accomplish similar results.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3ef1b15385b7386aabb1bf1b413dd3e8 https://doi.org/10.1109/hsi.2008.4581506 Zobrazit plný text záznamu