Selecting Classifiers for Medical Data Analysis

被引：0

作者：

Abin, Deepa ^{[1
]}

Potey, M. A. ^{[1
]}

机构：

[1] DY Patil Coll Engn, Dept Comp Engn, Pune, Maharashtra, India

来源：

2013 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE AND RESEARCH ADVANCEMENT (ICMIRA 2013) | 2013年

关键词：

Training set; test data; decision tree; error estimates;

D O I：

10.1109/ICMIRA.2013.60

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Applying data mining algorithms for knowledge discovery is being done in almost all domains but particularly in medical and healthcare domain it is still a challenge. Health issues are a major concern in the current world. Massive amount of high dimensional data is generated across the medical organizations. To discover knowledge for building efficient healthcare systems data mining algorithms play a crucial role. We have applied selected Decision tree classification algorithms for performing medical data analysis which is important in medical sector. We suggest appropriateness of the selected algorithms to the specific disease data.

引用

页码：285 / 289

页数：5

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