Breast cancer diagnosis using GA feature selection and Rotation Forest

Breast cancer is one of the primary causes of death among the women worldwide, and the accurate diagnosis is one of the most significant steps in breast cancer treatment. Data mining techniques can support doctors in diagnosis decision-making process. In this paper, we present different data mining...

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Veröffentlicht in:	Neural computing & applications Jg. 28; H. 4; S. 753 - 763
Hauptverfasser:	Aličković, Emina, Subasi, Abdulhamit
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.04.2017 Springer Nature B.V
Schlagworte:	Accuracy Artificial Intelligence Breast cancer Classification Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data mining Data Mining and Knowledge Discovery Decision making Diagnosis Feature extraction Genetic algorithms Image Processing and Computer Vision Medical diagnosis Original Article Probability and Statistics in Computer Science Rotation Genetic algorithm (GA) Logistic Regression Rotation Forest Support Vector Machine (SVM) Bayesian Network Decision Trees Radial Basis Function Networks (RBFN) Multilayer Perceptron (MLP) Breast cancer diagnosis Random Forest
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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