Support vector machines combined with feature selection for breast cancer diagnosis

By Mehmet Fatih Akay. Expert systems with applications, 2009

833 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fe83150bc326fd62d352cb2993ac91344f195e10

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. A. Ramli
2. Ahad Nasab
3. Ahmed M. Abdel-Zaher
4. Bo Jiang
5. Balakrishnan Narayanaswamy
6. Charles Peter Elkan
7. Syamsiah Mashohor
8. M. Iqbal Bin Saripan

In Artificial Intelligence

1. Zachary Chase Lipton
2. Balakrishnan Narayanaswamy
3. A. Ramli
4. Ahad Nasab
5. Ahmed M. Abdel-Zaher
6. Bo Jiang
7. Shohreh Kasaei
8. Dimitrios I. Fotiadis

In Molecular Biology

1. A. Ramli
2. Ahad Nasab
3. Ahmed M. Abdel-Zaher
4. Bo Jiang
5. Balakrishnan Narayanaswamy
6. Dimitrios I. Fotiadis
7. Charles Peter Elkan
8. Syamsiah Mashohor

Citations in the corpus

• Optimal Thresholding of Classifiers to Maximize F1 Measure (2014, methodology): “M(P,G) : {0, 1}n×m × {0, 1}n×m → R ∈ [0, 1].”
• Benign and malignant breast tumors classification based on region growing and CNN segmentation (2015, background): “Recent developments in digital mammography imaging systems have aimed to better diagnosis of abnormalities in the breast (Winsberg, Elkin, Macy, Bordaz, & Weymouth, 1967) and have increased the survival chance (Akay, 2009).”
• Breast cancer classification using deep belief networks (2016, methodology): “Ls-SVM, Akay (2009) paper Wisconsin 683 entries 99.51 100 97.91 80–20% Übeyli (2007) - SVM 99.54 – – 37–63% Dheeba et al. (2014)) - PSOWNN 93.67 94.17 92.11 – Mert et al. (2015)) - ICA-RBFNN 90.49 – – – Nahato et al. (2015)) - RS-BPNN 98.6 – – –”
• Machine learning applications in cancer prognosis and prediction (2014, background): “A grow-ingtrend is noted thelast decade in theuse of other supervised learning techniques,namely SVMsand BNs, towards cancerpredictionand prognosis [24,31 – 36].”
• SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier (2014, methodology): “The experimental step used in this study was first referred to the related study, ex, C = [1, 3, 10, 30, 100], [31]; then set a possible range for both databases (C = 1∼100, γ = 1∼12).”
• Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms (2014, methodology): “Akay (2009) proposed a SVM-based method combined with feature selection for breast cancer diagnosis.”
• Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. (2013, methodology): “An SVMbased method combined with feature selection has been developed for breast cancer diagnosis [54].”
• Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases (2011, methodology): “The original F-score is a simple filter technique which measures the discrimination of two sets of real numbers (Akay, 2009; Chen & Lin, 2005; Lee, 2009).”
• Evaluation of artificial intelligence techniques in disease diagnosis and prediction (2023, methodology): “An SVM algorithm combined with feature selection was used to diagnose breast cancer using WBCD and obtained 99.51% classification accuracy [97].”