The provision of a support vector machine for cancer detection
Saman Siadati *, M. Jafar Tarokh **
In this research, a learning approach to detect breast cancer is presented based on a supported vector machine model. Diagnosis plays an important role in determining the strategies of treatment that greatly affects the health of patients. Today, several classification models are proposed in the field of data mining with cancer diagnosis based on previous medical records of the patient. In these methods, the function of each algorithm depends on the different model settings, such as the types of input characteristics and model parameters. To counteract the individual performance limitations of the model, this cancer-based research uses a support-driven learner-based approach to reduce detection variance and increase its accuracy. In practice, the proposed method can be extended to other methods for the diagnosis of severe diseases such as cancer, which replaces the diagnostic methods with a safer, more reliable, and more robust methodology.
Data Analysis, Cancer Detection, Support Vector Machine, Learning Approach
*,** Strategic Intelligence Research Laboratory, K.N.T. university of technology, Tehran, Iran