Breast cancer diagnosis using GA feature selection and Rotation Forest

• Published in: Neural computing & applications Vol. 28; no. 4; pp. 753 - 763
• Main Authors: Aličković, Emina, Subasi, Abdulhamit
• Format: Journal Article
• Language: English
• Published: London Springer London 01.04.2017; Springer Nature B.V
• Subjects: 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

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...