A support vector machine approach to breast cancer diagnosis and prognosis

被引：0
|
作者：
Zafiropoulos, Elias [1 ]
Maglogiannis, Ilias [1 ]
Anagnostopoulos, Ioannis [1 ]
机构：
[1] Univ Aegean, Dept Informat & Commun Syst Engn, GR-83200 Karlovassi, Samos, Greece
来源：
ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS | 2006年 / 204卷
关键词：
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 recent years, computational diagnostic tools and artificial intelligence techniques provide automated procedures for objective judgments by making use of quantitative measures and machine learning. The paper presents a Support Vector Machine (SVM) approach for the prognosis and diagnosis of breast cancer implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and the Wisconsin Prognostic Breast Cancer (WPBC) datasets found in literature. The SVM algorithm performs excellently in both problems for the case study datasets, exhibiting high accuracy, sensitivity and specificity indices.
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页码：500 / +
页数：2
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