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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Neural computing & applications Ročník 28; číslo 4; s. 753 - 763
Hlavní autori: Aličković, Emina, Subasi, Abdulhamit
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.04.2017
Springer Nature B.V
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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 techniques for diagnosis of breast cancer. Two different Wisconsin Breast Cancer datasets have been used to evaluate the system proposed in this study. The proposed system has two stages. In the first stage, in order to eliminate insignificant features, genetic algorithms are used for extraction of informative and significant features. This process reduces the computational complexity and speed up the data mining process. In the second stage, several data mining techniques are employed to make a decision for two different categories of subjects with or without breast cancer. Different individual and multiple classifier systems were used in the second stage in order to construct accurate system for breast cancer classification. The performance of the methods is evaluated using classification accuracy, area under receiver operating characteristic curves and F -measure. Results obtained with the Rotation Forest model with GA-based 14 features show the highest classification accuracy (99.48 %), and when compared with the previous works, the proposed approach reveals the enhancement in performances. Results obtained in this study have potential to open new opportunities in diagnosis of breast cancer.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-015-2103-9