Breast cancer diagnosis using least square support vector machine

By Kemal Polat, Salih Günes. Digit. Signal Process., 2007

399 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/82d5b4635858db58fd38dd829ae5e2c4d13e3e66

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. Ahad Nasab
2. Bo Yang
3. Da-you Liu
4. Jie Liu
5. Nafiseh Ghaffar Nia
6. Huiling Chen
7. Erkan Kaplanoglu

In Biophysics

1. Ahad Nasab
2. Bo Yang
3. Da-you Liu
4. Jie Liu
5. Nafiseh Ghaffar Nia
6. Huiling Chen
7. Erkan Kaplanoglu

In Computer Vision and Pattern Recognition

1. Ahad Nasab
2. Bo Yang
3. Da-you Liu
4. Jie Liu
5. Nafiseh Ghaffar Nia
6. Huiling Chen
7. Erkan Kaplanoglu

Citations in the corpus

• Cartesian genetic programming encoded artificial neural networks: a comparison using three benchmarks (2013, neutral): “LS-SVM [36] No 1.”
• Evaluation of artificial intelligence techniques in disease diagnosis and prediction (2023, methodology): “The least-squares support vector machine (LSSVM) algorithm was successfully applied to WBCD to diagnose breast cancer and achieved 98.53% classification accuracy [95].”
• A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis (2011, methodology): “In Polat and Gunes (2007), least square SVM was used and accuracy of 98.53% was obtained.”