Prediction of Severe Brain Damage Outcome Using Two Data Mining Methods

被引：0

作者：

Grzymala-Busse, Jerzy W. ^{[1
]}

Hippe, Zdzislaw S. ^{[1
]}

Mroczek, Teresa

Bucinski, Adam ^{[2
]}

Strepikowska, Agnieszka ^{[3
,4
]}

Tutaj, Andrzej ^{[5
]}

机构：

[1] Univ Kansas, Dept Comp Sci, Lawrence, KS 66045 USA

[2] Univ Informat Technol & Management, Dept Expert Syst & Artifical Intelligence, Rzeszow, Poland

[3] Nicholas Copernicus Univ, Fac Pharm, Dept Biopharm, Bydgoszcz, Poland

[4] Neurol Clin, Olsztyn, Poland

[5] Specialist Hosp, Neurol Ward, Olsztyn, Poland

来源：

2008 CONFERENCE ON HUMAN SYSTEM INTERACTIONS, VOLS 1 AND 2 | 2008年

关键词：

Belief networks; BeliefSEEKER system; Glasgow Outcome Scale; LEM2 rule induction system; severe brain damage;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：591 / +

页数：3

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